Generating textured polygon strip hair from strand-based hair for a virtual character

ABSTRACT

Computer generated (CG) hair for a virtual character can include strand-based (instanced) hair in which many thousands of digital strands represent real human hair strands. Strand-based hair can appear highly realistic, but rendering strand-based hair in real-time presents challenges. Techniques for generating textured polygon strip (poly strip) hair for a virtual character can use as an input previously-generated strand-based hair for the virtual character. Poly strips can be generated for a sampled set of strands in the strand-based hair. Additional poly strips may be generated near hairlines or part lines. Hair textures from a hair texture library can be matched to the poly strips. The matched textures can be scaled and packed into a region of texture space (e.g., a square region), which provides improved computer access, efficiency, and speed. A rendering engine can use the poly strips and the packed hair textures to render the character&#39;s hair in real-time.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of priority to U.S. Patent Application No. 62/674,408, filed May 21, 2018, entitled GENERATING TEXTURED POLYGON STRIP HAIR FROM STRAND-BASED HAIR FOR A VIRTUAL CHARACTER, which is hereby incorporated by reference herein in its entirety.

FIELD

The present disclosure relates to virtual reality and augmented reality, including mixed reality, imaging and visualization systems and more particularly to systems and methods for converting strand-based hair of a virtual character into textured polygon strip (poly strip) hair for the virtual character.

BACKGROUND

Modem computing and display technologies have facilitated the development of systems for so called “virtual reality,” “augmented reality,” and “mixed reality” experiences, wherein digitally reproduced images are presented to a user in a manner such that they seem to be, or may be perceived as, real. A virtual reality (VR) scenario typically involves presentation of computer-generated virtual image information without transparency to other actual real-world visual input. An augmented reality (AR) scenario typically involves presentation of virtual image information as an augmentation to visualization of the actual world around the user. Mixed reality (MR) is a type of augmented reality in which physical and virtual objects may co-exist and interact in real time. Systems and methods disclosed herein address various challenges related to VR, AR and MR technology.

SUMMARY

Computer generated (CG) hair for a virtual character can include strand-based (also referred to as instanced) hair in which many thousands of digital strands represent real human hair strands. Strand-based hair can appear highly realistic, but rendering strand-based hair in real-time presents challenges. Techniques for generating textured polygon strip (poly strip) hair for a virtual character can use as an input previously-generated strand-based hair for the virtual character. Poly strips can be generated for a sampled set of strands in the strand-based hair. Additional poly strips may be generated near hairlines or part lines. Hair textures from a hair texture library can be matched to the poly strips. The matched textures can be scaled and packed into a region of texture space (e.g., a square region), which provides improved computer access, efficiency, and speed. A rendering engine can use the poly strips and the packed hair textures to render the character's hair in real-time.

The techniques described herein can be used to generate poly strip hair, fur, or any type of textured elements that can be applied to portions of humans, animals, fictitious characters, creatures, or objects.

Although certain embodiments of the systems and methods for avatar animation using poly strip hair are described in the context of augmented, mixed, or virtual reality, they are not so limited, and can be applied to gaming, movies, or visual effects (VFx).

Details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages will become apparent from the description, the drawings, and the claims. Neither this summary nor the following detailed description purports to define or limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages will become apparent from the description, the drawings, and the claims.

FIG. 1 depicts an illustration of a mixed reality scenario with certain virtual reality objects, and certain physical objects viewed by a person.

FIG. 2 schematically illustrates an example of a wearable system.

FIG. 3 schematically illustrates example components of a wearable system.

FIG. 4 schematically illustrates an example of a waveguide stack of a wearable device for outputting image information to a user.

FIG. 5 is a process flow diagram of an example of a method for interacting with a virtual user interface.

FIG. 6A is a block diagram of another example of a wearable system which can comprise an avatar processing and rendering system.

FIG. 6B illustrates example components of an avatar processing and rendering system.

FIG. 7 is a block diagram of an example of a wearable system including various inputs into the wearable system.

FIG. 8 is a process flow diagram of an example of a method of rendering virtual content in relation to recognized objects.

FIG. 9A schematically illustrates an overall system view depicting multiple wearable systems interacting with each other.

FIG. 9B illustrates an example telepresence session.

FIG. 10 illustrates an example of an avatar as perceived by a user of a wearable system.

FIG. 11A illustrates an example of an avatar showing examples of guide curves for generating strand-based (instanced) hair for the avatar.

FIG. 11B is a close-up view of a portion (shown in the dot-dash box) of the hair surface (e.g., scalp) of the avatar of FIG. 11A.

FIG. 11C illustrates an example of a hair mask (shown cross-hatched) that defines a region where hair will be generated on the avatar. The left panel is a 3D view of the avatar in physical space (e.g., xyz coordinates), and the right panel is a 2D view of the hair mask in texture space (e.g., UV coordinates).

FIG. 11D illustrates an example of a hair region having a hair part. The left panel shows a 3D space view and the right panel shows a texture space view.

FIG. 11E illustrates an avatar with an example of strand-based hair. The digital strands in the avatar's strand-based hair mimic real strands of human hair.

FIG. 12 illustrates an example of the generation of textured polygon strip (poly strip) hair for an avatar.

FIG. 13 illustrates an example of the generation of textured poly strip hair for an avatar based on pre-generated strand-based hair for the avatar.

FIG. 14 illustrates an example of packing multiple hair textures into a region of texture space. The region of texture space can be square (e.g., extending from 0 to 1 in each U, V coordinate).

FIG. 15A illustrates another example of an avatar showing examples of strands for strand-based (instanced) hair for the avatar.

FIG. 15B illustrates examples of a hairline and a part line for an avatar.

FIG. 15C illustrates an example of a poly strip generated for a guide curve.

FIG. 15D illustrates vectors that can be used to define an orientation for a poly strip generated for a strand.

FIG. 15E illustrates an example of an avatar showing placement of poly strips on the avatar's head.

FIG. 16A is a screenshot of an example texture library comprising a plurality of textures that can be applied to poly strips. In this example, the textures are hair textures.

FIG. 16B is a screenshot of the example texture library showing the textures that have been assigned to at least one of the poly strips for the avatar.

FIG. 16C illustrates a 3D view of an example of textured poly strip hair (left panel) and a texture space view of the texture applied to a particular poly strip (right panel).

FIG. 16D illustrates a 3D view of an example of textured poly strip hair (left panel) and a texture space view of all of the textures that are applied to at least one poly strip (right panel).

FIG. 17 is a flowchart that illustrates an example process for generating textured poly strip hair for an avatar based on previously-generated strand-based hair for the avatar.

Throughout the drawings, reference numbers may be re-used to indicate correspondence between referenced elements. The drawings are provided to illustrate example embodiments described herein and are not intended to limit the scope of the disclosure.

DETAILED DESCRIPTION Overview

A virtual avatar can be a virtual representation of a real or fictional person (or an animal, creature, character, or personified object) in an AR/VR/MR environment. For example, during a telepresence session in which two AR/VR/MR users are interacting with each other, a viewer can perceive an avatar of another user in the viewer's environment and thereby create a tangible sense of the other user's presence in the viewer's environment. The avatar can also provide a way for users to interact with each other and do things together in a shared virtual environment. For example, a student attending an online class can perceive and interact with avatars of other students or the teacher in a virtual classroom. As another example, a user playing a game in an AR/VR/MR environment may view and interact with avatars of other players or characters in the game.

Many virtual avatars have hair (or fur). To create a realistic ARNR/MR experience for a user, the hair (or fur) of the avatar advantageously should look and act like real hair. High-fidelity, computer-generated (CG) strand-based hair can be created for a virtual avatar. Strand-based hair uses a large number (e.g., hundreds of thousands) of digital strands of hair to represent the hair of a virtual character, similar to the strands of hair of a real person, and can look and behave very much like real hair. However, creation of computer-generated strand-based hair for a single avatar is a time-consuming process that typically involves many iterations and much manual input by a human animator. Creation of strand-based hair for many avatars is even more time-consuming. The amount of data needed to represent strand-based hair can be enormous, and the use of strand-based hair generally is ill-suited to real-time rendering of avatars in AR/MR/NR environments or games. For example, strand-based hair cannot be easily decimated or converted to a lower resolution format that is suitable for real-time rendering applications.

Accordingly, the systems and methods described herein allow generation of polygon strip (poly strip) hair from high-fidelity, strand-based hair created for a virtual avatar. The poly strip hair can include realistic hair textures and can be suitable for real-time rendering applications. A runtime engine (e.g., a rendering engine in an AR/MR/VR display) can access the poly strip hair data for an avatar and render realistic-looking and realistic-behaving hair in real time as the avatar moves around in and interacts with the AR/VR/MR environment. Rendering realistic-looking, poly strip hair for the avatar advantageously can reduce the likelihood that a human being who interacts with the avatar will enter the so-called uncanny valley, which represents a dip in human emotional response to an avatar that is almost, but not quite, human in its appearance or movements. Further, by generating the poly strip hair from previously-generated, high-fidelity strand-based hair for the avatar, the poly strip generation process advantageously can be automated according to a rule-based computer process and performed with fewer iterations and less time-intensive manual input by an animator.

Although the examples in this disclosure generally describe creating textured poly strip hair from strand-based hair for a human avatar (and in particular, hair on the avatar's head), analogous techniques can be applied to generate hair elsewhere on the human body or to generate hair or fur on animals, or hair or skin texture on fictitious creatures, and so forth.

Accordingly, a variety of implementations of systems and methods for generating poly strip hair from strand-based hair are described below.

Examples of 3D Display of a Wearable System

A wearable system (also referred to herein as an augmented reality (AR) system) can be configured to present two-dimensional (2D) or three-dimensional (3D) virtual images to a user. The images may be still images, frames of a video, or a video, in combination or the like. At least a portion of the wearable system can be implemented on a wearable device that can present a VR, AR, or MR environment, alone or in combination, for user interaction. The wearable device can be used interchangeably as an AR device (ARD). Further, for the purpose of the present disclosure, the term “AR” is used interchangeably with the term “MR”.

FIG. 1 depicts an illustration of a mixed reality scenario with certain virtual reality objects, and certain physical objects viewed by a person. In FIG. 1, an MR scene 100 is depicted wherein a user of an MR technology sees a real-world park-like setting 110 featuring people, trees, buildings in the background, and a concrete platform 120. In addition to these items, the user of the MR technology also perceives that he “sees” a robot statue 130 standing upon the real-world platform 120, and a cartoon-like avatar character 140 flying by which seems to be a personification of a bumble bee, even though these elements do not exist in the real world.

In order for the 3D display to produce a true sensation of depth, and more specifically, a simulated sensation of surface depth, it may be desirable for each point in the display's visual field to generate an accommodative response corresponding to its virtual depth. If the accommodative response to a display point does not correspond to the virtual depth of that point, as determined by the binocular depth cues of convergence and stereopsis, the human eye may experience an accommodation conflict, resulting in unstable imaging, harmful eye strain, headaches, and, in the absence of accommodation information, almost a complete lack of surface depth.

VR, AR, and MR experiences can be provided by display systems having displays in which images corresponding to a plurality of depth planes are provided to a viewer. The images may be different for each depth plane (e.g., provide slightly different presentations of a scene or object) and may be separately focused by the viewer's eyes, thereby helping to provide the user with depth cues based on the accommodation of the eye required to bring into focus different image features for the scene located on different depth plane or based on observing different image features on different depth planes being out of focus. As discussed elsewhere herein, such depth cues provide credible perceptions of depth.

FIG. 2 illustrates an example of wearable system 200 which can be configured to provide an AR/VR/MR scene. The wearable system 200 can also be referred to as the AR system 200. The wearable system 200 includes a display 220, and various mechanical and electronic modules and systems to support the functioning of display 220. The display 220 may be coupled to a frame 230, which is wearable by a user, wearer, or viewer 210. The display 220 can be positioned in front of the eyes of the user 210. The display 220 can present AR/VR/MR content to a user. The display 220 can comprise a head mounted display (HMD) that is worn on the head of the user.

In some embodiments, a speaker 240 is coupled to the frame 230 and positioned adjacent the ear canal of the user (in some embodiments, another speaker, not shown, is positioned adjacent the other ear canal of the user to provide for stereo/shapeable sound control). The display 220 can include an audio sensor (e.g., a microphone) 232 for detecting an audio stream from the environment and capture ambient sound. In some embodiments, one or more other audio sensors, not shown, are positioned to provide stereo sound reception. Stereo sound reception can be used to determine the location of a sound source. The wearable system 200 can perform voice or speech recognition on the audio stream.

The wearable system 200 can include an outward-facing imaging system 464 (shown in FIG. 4) which observes the world in the environment around the user. The wearable system 200 can also include an inward-facing imaging system 462 (shown in FIG. 4) which can track the eye movements of the user. The inward-facing imaging system may track either one eye's movements or both eyes' movements. The inward-facing imaging system 462 may be attached to the frame 230 and may be in electrical communication with the processing modules 260 or 270, which may process image information acquired by the inward-facing imaging system to determine, e.g., the pupil diameters or orientations of the eyes, eye movements or eye pose of the user 210. The inward-facing imaging system 462 may include one or more cameras. For example, at least one camera may be used to image each eye. The images acquired by the cameras may be used to determine pupil size or eye pose for each eye separately, thereby allowing presentation of image information to each eye to be dynamically tailored to that eye.

As an example, the wearable system 200 can use the outward-facing imaging system 464 or the inward-facing imaging system 462 to acquire images of a pose of the user. The images may be still images, frames of a video, or a video.

The display 220 can be operatively coupled 250, such as by a wired lead or wireless connectivity, to a local data processing module 260 which may be mounted in a variety of configurations, such as fixedly attached to the frame 230, fixedly attached to a helmet or hat worn by the user, embedded in headphones, or otherwise removably attached to the user 210 (e.g., in a backpack-style configuration, in a belt-coupling style configuration).

The local processing and data module 260 may comprise a hardware processor, as well as digital memory, such as non-volatile memory (e.g., flash memory), both of which may be utilized to assist in the processing, caching, and storage of data. The data may include data a) captured from sensors (which may be, e.g., operatively coupled to the frame 230 or otherwise attached to the user 210), such as image capture devices (e.g., cameras in the inward-facing imaging system or the outward-facing imaging system), audio sensors (e.g., microphones), inertial measurement units (IMUs), accelerometers, compasses, global positioning system (GPS) units, radio devices, or gyroscopes; or b) acquired or processed using remote processing module 270 or remote data repository 280, possibly for passage to the display 220 after such processing or retrieval. The local processing and data module 260 may be operatively coupled by communication links 262 or 264, such as via wired or wireless communication links, to the remote processing module 270 or remote data repository 280 such that these remote modules are available as resources to the local processing and data module 260. In addition, remote processing module 280 and remote data repository 280 may be operatively coupled to each other.

In some embodiments, the remote processing module 270 may comprise one or more processors configured to analyze and process data or image information. In some embodiments, the remote data repository 280 may comprise a digital data storage facility, which may be available through the internet or other networking configuration in a “cloud” resource configuration. In some embodiments, all data is stored and all computations are performed in the local processing and data module, allowing fully autonomous use from a remote module.

Example Components of a Wearable System

FIG. 3 schematically illustrates example components of a wearable system. FIG. 3 shows a wearable system 200 which can include a display 220 and a frame 230. A blown-up view 202 schematically illustrates various components of the wearable system 200. In certain implements, one or more of the components illustrated in FIG. 3 can be part of the display 220. The various components alone or in combination can collect a variety of data (such as e.g., audio or visual data) associated with the user of the wearable system 200 or the user's environment. It should be appreciated that other embodiments may have additional or fewer components depending on the application for which the wearable system is used. Nevertheless, FIG. 3 provides a basic idea of some of the various components and types of data that may be collected, analyzed, and stored through the wearable system.

FIG. 3 shows an example wearable system 200 which can include the display 220. The display 220 can comprise a display lens 226 that may be mounted to a user's head or a housing or frame 230, which corresponds to the frame 230. The display lens 226 may comprise one or more transparent mirrors positioned by the housing 230 in front of the user's eyes 302, 304 and may be configured to bounce projected light 338 into the eyes 302, 304 and facilitate beam shaping, while also allowing for transmission of at least some light from the local environment. The wavefront of the projected light beam 338 may be bent or focused to coincide with a desired focal distance of the projected light. As illustrated, two wide-field-of-view machine vision cameras 316 (also referred to as world cameras) can be coupled to the housing 230 to image the environment around the user. These cameras 316 can be dual capture visible light/non-visible (e.g., infrared) light cameras. The cameras 316 may be part of the outward-facing imaging system 464 shown in FIG. 4. Image acquired by the world cameras 316 can be processed by the pose processor 336. For example, the pose processor 336 can implement one or more object recognizers 708 (e.g., shown in FIG. 7) to identify a pose of a user or another person in the user's environment or to identify a physical object in the user's environment.

With continued reference to FIG. 3, a pair of scanned-laser shaped-wavefront (e.g., for depth) light projector modules with display mirrors and optics configured to project light 338 into the eyes 302, 304 are shown. The depicted view also shows two miniature infrared cameras 324 paired with infrared light (such as light emitting diodes “LED”s), which are configured to be able to track the eyes 302, 304 of the user to support rendering and user input. The cameras 324 may be part of the inward-facing imaging system 462 shown in FIG. 4 The wearable system 200 can further feature a sensor assembly 339, which may comprise X, Y, and Z axis accelerometer capability as well as a magnetic compass and X, Y, and Z axis gyro capability, preferably providing data at a relatively high frequency, such as 200 Hz. The sensor assembly 339 may be part of the IMU described with reference to FIG. 2A The depicted system 200 can also comprise a head pose processor 336, such as an ASIC (application specific integrated circuit), FPGA (field programmable gate array), or ARM processor (advanced reduced-instruction-set machine), which may be configured to calculate real or near-real time user head pose from wide field of view image information output from the capture devices 316. The head pose processor 336 can be a hardware processor and can be implemented as part of the local processing and data module 260 shown in FIG. 2A.

The wearable system can also include one or more depth sensors 234. The depth sensor 234 can be configured to measure the distance between an object in an environment to a wearable device. The depth sensor 234 may include a laser scanner (e.g., a lidar), an ultrasonic depth sensor, or a depth sensing camera. In certain implementations, where the cameras 316 have depth sensing ability, the cameras 316 may also be considered as depth sensors 234.

Also shown is a processor 332 configured to execute digital or analog processing to derive pose from the gyro, compass, or accelerometer data from the sensor assembly 339. The processor 332 may be part of the local processing and data module 260 shown in FIG. 2. The wearable system 200 as shown in FIG. 3 can also include a position system such as, e.g., a GPS 337 (global positioning system) to assist with pose and positioning analyses. In addition, the GPS may further provide remotely-based (e.g., cloud-based) information about the user's environment. This information may be used for recognizing objects or information in user's environment.

The wearable system may combine data acquired by the GPS 337 and a remote computing system (such as, e.g., the remote processing module 270, another user's ARD, etc.) which can provide more information about the user's environment. As one example, the wearable system can determine the user's location based on GPS data and retrieve a world map (e.g., by communicating with a remote processing module 270) including virtual objects associated with the user's location. As another example, the wearable system 200 can monitor the environment using the world cameras 316 (which may be part of the outward-facing imaging system 464 shown in FIG. 4). Based on the images acquired by the world cameras 316, the wearable system 200 can detect objects in the environment (e.g., by using one or more object recognizers 708 shown in FIG. 7). The wearable system can further use data acquired by the GPS 337 to interpret the characters.

The wearable system 200 may also comprise a rendering engine 334 which can be configured to provide rendering information that is local to the user to facilitate operation of the scanners and imaging into the eyes of the user, for the user's view of the world. The rendering engine 334 may be implemented by a hardware processor (such as, e.g., a central processing unit or a graphics processing unit). In some embodiments, the rendering engine is part of the local processing and data module 260. The rendering engine 334 can be communicatively coupled (e.g., via wired or wireless links) to other components of the wearable system 200. For example, the rendering engine 334, can be coupled to the eye cameras 324 via communication link 274, and be coupled to a projecting subsystem 318 (which can project light into user's eyes 302, 304 via a scanned laser arrangement in a manner similar to a retinal scanning display) via the communication link 272. The rendering engine 334 can also be in communication with other processing units such as, e.g., the sensor pose processor 332 and the image pose processor 336 via links 276 and 294 respectively.

The cameras 324 (e.g., mini infrared cameras) may be utilized to track the eye pose to support rendering and user input. Some example eye poses may include where the user is looking or at what depth he or she is focusing (which may be estimated with eye vergence). The GPS 337, gyros, compass, and accelerometers 339 may be utilized to provide coarse or fast pose estimates. One or more of the cameras 316 can acquire images and pose, which in conjunction with data from an associated cloud computing resource, may be utilized to map the local environment and share user views with others.

The example components depicted in FIG. 3 are for illustration purposes only. Multiple sensors and other functional modules are shown together for ease of illustration and description. Some embodiments may include only one or a subset of these sensors or modules. Further, the locations of these components are not limited to the positions depicted in FIG. 3. Some components may be mounted to or housed within other components, such as a belt-mounted component, a hand-held component, or a helmet component. As one example, the image pose processor 336, sensor pose processor 332, and rendering engine 334 may be positioned in a beltpack and configured to communicate with other components of the wearable system via wireless communication, such as ultra-wideband, Wi-Fi, Bluetooth, etc., or via wired communication. The depicted housing 230 preferably is head-mountable and wearable by the user. However, some components of the wearable system 200 may be worn to other portions of the user's body. For example, the speaker 240 may be inserted into the ears of a user to provide sound to the user.

Regarding the projection of light 338 into the eyes 302, 304 of the user, in some embodiment, the cameras 324 may be utilized to measure where the centers of a user's eyes are geometrically verged to, which, in general, coincides with a position of focus, or “depth of focus”, of the eyes. A 3-dimensional surface of all points the eyes verge to can be referred to as the “horopter”. The focal distance may take on a finite number of depths, or may be infinitely varying. Light projected from the vergence distance appears to be focused to the subject eye 302, 304, while light in front of or behind the vergence distance is blurred. Examples of wearable devices and other display systems of the present disclosure are also described in U.S. Patent Publication No. 2016/0270656, which is incorporated by reference herein in its entirety.

The human visual system is complicated and providing a realistic perception of depth is challenging. Viewers of an object may perceive the object as being three-dimensional due to a combination of vergence and accommodation. Vergence movements (e.g., rolling movements of the pupils toward or away from each other to converge the lines of sight of the eyes to fixate upon an object) of the two eyes relative to each other are closely associated with focusing (or “accommodation”) of the lenses of the eyes. Under normal conditions, changing the focus of the lenses of the eyes, or accommodating the eyes, to change focus from one object to another object at a different distance will automatically cause a matching change in vergence to the same distance, under a relationship known as the “accommodation-vergence reflex.” Likewise, a change in vergence will trigger a matching change in accommodation, under normal conditions. Display systems that provide a better match between accommodation and vergence may form more realistic and comfortable simulations of three-dimensional imagery.

Further spatially coherent light with a beam diameter of less than about 0.7 millimeters can be correctly resolved by the human eye regardless of where the eye focuses. Thus, to create an illusion of proper focal depth, the eye vergence may be tracked with the cameras 324, and the rendering engine 334 and projection subsystem 318 may be utilized to render all objects on or close to the horopter in focus, and all other objects at varying degrees of defocus (e.g., using intentionally-created blurring). Preferably, the system 220 renders to the user at a frame rate of about 60 frames per second or greater. As described above, preferably, the cameras 324 may be utilized for eye tracking, and software may be configured to pick up not only vergence geometry but also focus location cues to serve as user inputs. Preferably, such a display system is configured with brightness and contrast suitable for day or night use.

In some embodiments, the display system preferably has latency of less than about 20 milliseconds for visual object alignment, less than about 0.1 degree of angular alignment, and about 1 arc minute of resolution, which, without being limited by theory, is believed to be approximately the limit of the human eye. The display system 220 may be integrated with a localization system, which may involve GPS elements, optical tracking, compass, accelerometers, or other data sources, to assist with position and pose determination; localization information may be utilized to facilitate accurate rendering in the user's view of the pertinent world (e.g., such information would facilitate the glasses to know where they are with respect to the real world).

In some embodiments, the wearable system 200 is configured to display one or more virtual images based on the accommodation of the user's eyes. Unlike prior 3D display approaches that force the user to focus where the images are being projected, in some embodiments, the wearable system is configured to automatically vary the focus of projected virtual content to allow for a more comfortable viewing of one or more images presented to the user. For example, if the user's eyes have a current focus of 1 m, the image may be projected to coincide with the user's focus. If the user shifts focus to 3 m, the image is projected to coincide with the new focus. Thus, rather than forcing the user to a predetermined focus, the wearable system 200 of some embodiments allows the user's eye to a function in a more natural manner.

Such a wearable system 200 may eliminate or reduce the incidences of eye strain, headaches, and other physiological symptoms typically observed with respect to virtual reality devices. To achieve this, various embodiments of the wearable system 200 are configured to project virtual images at varying focal distances, through one or more variable focus elements (VFEs). In one or more embodiments, 3D perception may be achieved through a multi-plane focus system that projects images at fixed focal planes away from the user. Other embodiments employ variable plane focus, wherein the focal plane is moved back and forth in the z-direction to coincide with the user's present state of focus.

In both the multi-plane focus systems and variable plane focus systems, wearable system 200 may employ eye tracking to determine a vergence of the user's eyes, determine the user's current focus, and project the virtual image at the determined focus. In other embodiments, wearable system 200 comprises a light modulator that variably projects, through a fiber scanner, or other light generating source, light beams of varying focus in a raster pattern across the retina. Thus, the ability of the display of the wearable system 200 to project images at varying focal distances not only eases accommodation for the user to view objects in 3D, but may also be used to compensate for user ocular anomalies, as further described in U.S. Patent Publication No. 2016/0270656, which is incorporated by reference herein in its entirety. In some other embodiments, a spatial light modulator may project the images to the user through various optical components. For example, as described further below, the spatial light modulator may project the images onto one or more waveguides, which then transmit the images to the user.

Waveguide Stack Assembly

FIG. 4 illustrates an example of a waveguide stack for outputting image information to a user. A wearable system 400 includes a stack of waveguides, or stacked waveguide assembly 480 that may be utilized to provide three-dimensional perception to the eye/brain using a plurality of waveguides 432 b, 434 b, 436 b, 438 b, 4400 b. In some embodiments, the wearable system 400 may correspond to wearable system 200 of FIG. 2, with FIG. 4 schematically showing some parts of that wearable system 200 in greater detail. For example, in some embodiments, the waveguide assembly 480 may be integrated into the display 220 of FIG. 2.

With continued reference to FIG. 4, the waveguide assembly 480 may also include a plurality of features 458, 456, 454, 452 between the waveguides. In some embodiments, the features 458, 456, 454, 452 may be lenses. In other embodiments, the features 458, 456, 454, 452 may not be lenses. Rather, they may simply be spacers (e.g., cladding layers or structures for forming air gaps).

The waveguides 432 b, 434 b, 436 b, 438 b, 440 b or the plurality of lenses 458, 456, 454, 452 may be configured to send image information to the eye with various levels of wavefront curvature or light ray divergence. Each waveguide level may be associated with a particular depth plane and may be configured to output image information corresponding to that depth plane. Image injection devices 420, 422, 424, 426, 428 may be utilized to inject image information into the waveguides 440 b, 438 b, 436 b, 434 b, 432 b, each of which may be configured to distribute incoming light across each respective waveguide, for output toward the eye 410. Light exits an output surface of the image injection devices 420, 422, 424, 426, 428 and is injected into a corresponding input edge of the waveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some embodiments, a single beam of light (e.g., a collimated beam) may be injected into each waveguide to output an entire field of cloned collimated beams that are directed toward the eye 410 at particular angles (and amounts of divergence) corresponding to the depth plane associated with a particular waveguide.

In some embodiments, the image injection devices 420, 422, 424, 426, 428 are discrete displays that each produce image information for injection into a corresponding waveguide 440 b, 438 b, 436 b, 434 b, 432 b, respectively. In some other embodiments, the image injection devices 420, 422, 424, 426, 428 are the output ends of a single multiplexed display which may, e.g., pipe image information via one or more optical conduits (such as fiber optic cables) to each of the image injection devices 420, 422, 424, 426, 428.

A controller 460 controls the operation of the stacked waveguide assembly 480 and the image injection devices 420, 422, 424, 426, 428. The controller 460 includes programming (e.g., instructions in a non-transitory computer-readable medium) that regulates the timing and provision of image information to the waveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some embodiments, the controller 460 may be a single integral device, or a distributed system connected by wired or wireless communication channels. The controller 460 may be part of the processing modules 260 or 270 (illustrated in FIG. 2) in some embodiments.

The waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be configured to propagate light within each respective waveguide by total internal reflection (TIR). The waveguides 440 b, 438 b, 436 b, 434 b, 432 b may each be planar or have another shape (e.g., curved), with major top and bottom surfaces and edges extending between those major top and bottom surfaces. In the illustrated configuration, the waveguides 440 b, 438 b, 436 b, 434 b, 432 b may each include light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a that are configured to extract light out of a waveguide by redirecting the light, propagating within each respective waveguide, out of the waveguide to output image information to the eye 410. Extracted light may also be referred to as outcoupled light, and light extracting optical elements may also be referred to as outcoupling optical elements. An extracted beam of light is outputted by the waveguide at locations at which the light propagating in the waveguide strikes a light redirecting element. The light extracting optical elements (440 a, 438 a, 436 a, 434 a, 432 a) may, for example, be reflective or diffractive optical features. While illustrated disposed at the bottom major surfaces of the waveguides 440 b, 438 b, 436 b, 434 b, 432 b for ease of description and drawing clarity, in some embodiments, the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a may be disposed at the top or bottom major surfaces, or may be disposed directly in the volume of the waveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some embodiments, the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a may be formed in a layer of material that is attached to a transparent substrate to form the waveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some other embodiments, the waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be a monolithic piece of material and the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a may be formed on a surface or in the interior of that piece of material.

With continued reference to FIG. 4, as discussed herein, each waveguide 440 b, 438 b, 436 b, 434 b, 432 b is configured to output light to form an image corresponding to a particular depth plane. For example, the waveguide 432 b nearest the eye may be configured to deliver collimated light, as injected into such waveguide 432 b, to the eye 410. The collimated light may be representative of the optical infinity focal plane. The next waveguide up 434 b may be configured to send out collimated light which passes through the first lens 452 (e.g., a negative lens) before it can reach the eye 410. First lens 452 may be configured to create a slight convex wavefront curvature so that the eye/brain interprets light coming from that next waveguide up 434 b as coming from a first focal plane closer inward toward the eye 410 from optical infinity. Similarly, the third up waveguide 436 b passes its output light through both the first lens 452 and second lens 454 before reaching the eye 410. The combined optical power of the first and second lenses 452 and 454 may be configured to create another incremental amount of wavefront curvature so that the eye/brain interprets light coming from the third waveguide 436 b as coming from a second focal plane that is even closer inward toward the person from optical infinity than was light from the next waveguide up 434 b.

The other waveguide layers (e.g., waveguides 438 b, 440 b) and lenses (e.g., lenses 456, 458) are similarly configured, with the highest waveguide 440 b in the stack sending its output through all of the lenses between it and the eye for an aggregate focal power representative of the closest focal plane to the person. To compensate for the stack of lenses 458, 456, 454, 452 when viewing/interpreting light coming from the world 470 on the other side of the stacked waveguide assembly 480, a compensating lens layer 430 may be disposed at the top of the stack to compensate for the aggregate power of the lens stack 458, 456, 454, 452 below. Such a configuration provides as many perceived focal planes as there are available waveguide/lens pairings. Both the light extracting optical elements of the waveguides and the focusing aspects of the lenses may be static (e.g., not dynamic or electro-active). In some alternative embodiments, either or both may be dynamic using electro-active features.

With continued reference to FIG. 4, the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a may be configured to both redirect light out of their respective waveguides and to output this light with the appropriate amount of divergence or collimation for a particular depth plane associated with the waveguide. As a result, waveguides having different associated depth planes may have different configurations of light extracting optical elements, which output light with a different amount of divergence depending on the associated depth plane. In some embodiments, as discussed herein, the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a may be volumetric or surface features, which may be configured to output light at specific angles. For example, the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a may be volume holograms, surface holograms, and/or diffraction gratings. Light extracting optical elements, such as diffraction gratings, are described in U.S. Patent Publication No. 2015/0178939, published Jun. 25, 2015, which is incorporated by reference herein in its entirety.

In some embodiments, the light extracting optical elements 440 a, 438 a, 436 a, 434 a, 432 a are diffractive features that form a diffraction pattern, or “diffractive optical element” (also referred to herein as a “DOE”). Preferably, the DOE has a relatively low diffraction efficiency so that only a portion of the light of the beam is deflected away toward the eye 410 with each intersection of the DOE, while the rest continues to move through a waveguide via total internal reflection. The light carrying the image information can thus be divided into a number of related exit beams that exit the waveguide at a multiplicity of locations and the result is a fairly uniform pattern of exit emission toward the eye 304 for this particular collimated beam bouncing around within a waveguide.

In some embodiments, one or more DOEs may be switchable between “on” state in which they actively diffract, and “off” state in which they do not significantly diffract. For instance, a switchable DOE may comprise a layer of polymer dispersed liquid crystal, in which microdroplets comprise a diffraction pattern in a host medium, and the refractive index of the microdroplets can be switched to substantially match the refractive index of the host material (in which case the pattern does not appreciably diffract incident light) or the microdroplet can be switched to an index that does not match that of the host medium (in which case the pattern actively diffracts incident light).

In some embodiments, the number and distribution of depth planes or depth of field may be varied dynamically based on the pupil sizes or orientations of the eyes of the viewer. Depth of field may change inversely with a viewer's pupil size. As a result, as the sizes of the pupils of the viewer's eyes decrease, the depth of field increases such that one plane that is not discernible because the location of that plane is beyond the depth of focus of the eye may become discernible and appear more in focus with reduction of pupil size and commensurate with the increase in depth of field. Likewise, the number of spaced apart depth planes used to present different images to the viewer may be decreased with the decreased pupil size. For example, a viewer may not be able to clearly perceive the details of both a first depth plane and a second depth plane at one pupil size without adjusting the accommodation of the eye away from one depth plane and to the other depth plane. These two depth planes may, however, be sufficiently in focus at the same time to the user at another pupil size without changing accommodation.

In some embodiments, the display system may vary the number of waveguides receiving image information based upon determinations of pupil size or orientation, or upon receiving electrical signals indicative of particular pupil size or orientation. For example, if the user's eyes are unable to distinguish between two depth planes associated with two waveguides, then the controller 460 (which may be an embodiment of the local processing and data module 260) can be configured or programmed to cease providing image information to one of these waveguides. Advantageously, this may reduce the processing burden on the system, thereby increasing the responsiveness of the system. In embodiments in which the DOEs for a waveguide are switchable between the on and off states, the DOEs may be switched to the off state when the waveguide does receive image information.

In some embodiments, it may be desirable to have an exit beam meet the condition of having a diameter that is less than the diameter of the eye of a viewer. However, meeting this condition may be challenging in view of the variability in size of the viewer's pupils. In some embodiments, this condition is met over a wide range of pupil sizes by varying the size of the exit beam in response to determinations of the size of the viewer's pupil. For example, as the pupil size decreases, the size of the exit beam may also decrease. In some embodiments, the exit beam size may be varied using a variable aperture.

The wearable system 400 can include an outward-facing imaging system 464 (e.g., a digital camera) that images a portion of the world 470. This portion of the world 470 may be referred to as the field of view (FOV) of a world camera and the imaging system 464 is sometimes referred to as an FOV camera. The FOV of the world camera may or may not be the same as the FOV of a viewer 210 which encompasses a portion of the world 470 the viewer 210 perceives at a given time. For example, in some situations, the FOV of the world camera may be larger than the viewer 210 of the viewer 210 of the wearable system 400. The entire region available for viewing or imaging by a viewer may be referred to as the field of regard (FOR). The FOR may include 4π steradians of solid angle surrounding the wearable system 400 because the wearer can move his body, head, or eyes to perceive substantially any direction in space. In other contexts, the wearer's movements may be more constricted, and accordingly the wearer's FOR may subtend a smaller solid angle. Images obtained from the outward-facing imaging system 464 can be used to track gestures made by the user (e.g., hand or finger gestures), detect objects in the world 470 in front of the user, and so forth.

The wearable system 400 can include an audio sensor 232, e.g., a microphone, to capture ambient sound. As described above, in some embodiments, one or more other audio sensors can be positioned to provide stereo sound reception useful to the determination of location of a speech source. The audio sensor 232 can comprise a directional microphone, as another example, which can also provide such useful directional information as to where the audio source is located. The wearable system 400 can use information from both the outward-facing imaging system 464 and the audio sensor 230 in locating a source of speech, or to determine an active speaker at a particular moment in time, etc. For example, the wearable system 400 can use the voice recognition alone or in combination with a reflected image of the speaker (e.g., as seen in a mirror) to determine the identity of the speaker. As another example, the wearable system 400 can determine a position of the speaker in an environment based on sound acquired from directional microphones. The wearable system 400 can parse the sound coming from the speaker's position with speech recognition algorithms to determine the content of the speech and use voice recognition techniques to determine the identity (e.g., name or other demographic information) of the speaker.

The wearable system 400 can also include an inward-facing imaging system 466 (e.g., a digital camera), which observes the movements of the user, such as the eye movements and the facial movements. The inward-facing imaging system 466 may be used to capture images of the eye 410 to determine the size and/or orientation of the pupil of the eye 304. The inward-facing imaging system 466 can be used to obtain images for use in determining the direction the user is looking (e.g., eye pose) or for biometric identification of the user (e.g., via iris identification). In some embodiments, at least one camera may be utilized for each eye, to separately determine the pupil size or eye pose of each eye independently, thereby allowing the presentation of image information to each eye to be dynamically tailored to that eye. In some other embodiments, the pupil diameter or orientation of only a single eye 410 (e.g., using only a single camera per pair of eyes) is determined and assumed to be similar for both eyes of the user. The images obtained by the inward-facing imaging system 466 may be analyzed to determine the user's eye pose or mood, which can be used by the wearable system 400 to decide which audio or visual content should be presented to the user. The wearable system 400 may also determine head pose (e.g., head position or head orientation) using sensors such as IMUs, accelerometers, gyroscopes, etc.

The wearable system 400 can include a user input device 466 by which the user can input commands to the controller 460 to interact with the wearable system 400. For example, the user input device 466 can include a trackpad, a touchscreen, a joystick, a multiple degree-of-freedom (DOF) controller, a capacitive sensing device, a game controller, a keyboard, a mouse, a directional pad (D-pad), a wand, a haptic device, a totem (e.g., functioning as a virtual user input device), and so forth. A multi-DOF controller can sense user input in some or all possible translations (e.g., left/right, forward/backward, or up/down) or rotations (e.g., yaw, pitch, or roll) of the controller. A multi-DOF controller which supports the translation movements may be referred to as a 3DOF while a multi-DOF controller which supports the translations and rotations may be referred to as 6DOF. In some cases, the user may use a finger (e.g., a thumb) to press or swipe on a touch-sensitive input device to provide input to the wearable system 400 (e.g., to provide user input to a user interface provided by the wearable system 400). The user input device 466 may be held by the user's hand during the use of the wearable system 400. The user input device 466 can be in wired or wireless communication with the wearable system 400.

Other Components of the Wearable System

In many implementations, the wearable system may include other components in addition or in alternative to the components of the wearable system described above. The wearable system may, for example, include one or more haptic devices or components. The haptic devices or components may be operable to provide a tactile sensation to a user. For example, the haptic devices or components may provide a tactile sensation of pressure or texture when touching virtual content (e.g., virtual objects, virtual tools, other virtual constructs). The tactile sensation may replicate a feel of a physical object which a virtual object represents, or may replicate a feel of an imagined object or character (e.g., a dragon) which the virtual content represents. In some implementations, haptic devices or components may be worn by the user (e.g., a user wearable glove). In some implementations, haptic devices or components may be held by the user.

The wearable system may, for example, include one or more physical objects which are manipulable by the user to allow input or interaction with the wearable system. These physical objects may be referred to herein as totems. Some totems may take the form of inanimate objects, such as for example, a piece of metal or plastic, a wall, a surface of table. In certain implementations, the totems may not actually have any physical input structures (e.g., keys, triggers, joystick, trackball, rocker switch). Instead, the totem may simply provide a physical surface, and the wearable system may render a user interface so as to appear to a user to be on one or more surfaces of the totem. For example, the wearable system may render an image of a computer keyboard and trackpad to appear to reside on one or more surfaces of a totem. For example, the wearable system may render a virtual computer keyboard and virtual trackpad to appear on a surface of a thin rectangular plate of aluminum which serves as a totem. The rectangular plate does not itself have any physical keys or trackpad or sensors. However, the wearable system may detect user manipulation or interaction or touches with the rectangular plate as selections or inputs made via the virtual keyboard or virtual trackpad. The user input device 466 (shown in FIG. 4) may be an embodiment of a totem, which may include a trackpad, a touchpad, a trigger, a joystick, a trackball, a rocker or virtual switch, a mouse, a keyboard, a multi-degree-of-freedom controller, or another physical input device. A user may use the totem, alone or in combination with poses, to interact with the wearable system or other users.

Examples of haptic devices and totems usable with the wearable devices, HMD, and display systems of the present disclosure are described in U.S. Patent Publication No. 2015/0016777, which is incorporated by reference herein in its entirety.

Example Processes of User Interactions with a Wearable System

FIG. 5 is a process flow diagram of an example of a method 500 for interacting with a virtual user interface. The method 500 may be performed by the wearable system described herein. Embodiments of the method 500 can be used by the wearable system to detect persons or documents in the FOV of the wearable system.

At block 510, the wearable system may identify a particular UI. The type of UI may be predetermined by the user. The wearable system may identify that a particular UI needs to be populated based on a user input (e.g., gesture, visual data, audio data, sensory data, direct command, etc.). The UI can be specific to a security scenario where the wearer of the system is observing users who present documents to the wearer (e.g., at a travel checkpoint). At block 520, the wearable system may generate data for the virtual UI. For example, data associated with the confines, general structure, shape of the UI etc., may be generated. In addition, the wearable system may determine map coordinates of the user's physical location so that the wearable system can display the UI in relation to the user's physical location. For example, if the UI is body centric, the wearable system may determine the coordinates of the user's physical stance, head pose, or eye pose such that a ring UI can be displayed around the user or a planar UI can be displayed on a wall or in front of the user. In the security context described herein, the UI may be displayed as if the UI were surrounding the traveler who is presenting documents to the wearer of the system, so that the wearer can readily view the UI while looking at the traveler and the traveler's documents. If the UI is hand centric, the map coordinates of the user's hands may be determined. These map points may be derived through data received through the FOV cameras, sensory input, or any other type of collected data.

At block 530, the wearable system may send the data to the display from the cloud or the data may be sent from a local database to the display components. At block 540, the UI is displayed to the user based on the sent data. For example, a light field display can project the virtual UI into one or both of the user's eyes. Once the virtual UI has been created, the wearable system may simply wait for a command from the user to generate more virtual content on the virtual UI at block 550. For example, the UI may be a body centric ring around the user's body or the body of a person in the user's environment (e.g., a traveler). The wearable system may then wait for the command (a gesture, a head or eye movement, voice command, input from a user input device, etc.), and if it is recognized (block 560), virtual content associated with the command may be displayed to the user (block 570).

Examples of Avatar Rendering in Mixed Reality

A wearable system may employ various mapping related techniques in order to achieve high depth of field in the rendered light fields. In mapping out the virtual world, it is advantageous to know all the features and points in the real world to accurately portray virtual objects in relation to the real world. To this end, FOV images captured from users of the wearable system can be added to a world model by including new pictures that convey information about various points and features of the real world. For example, the wearable system can collect a set of map points (such as 2D points or 3D points) and find new map points to render a more accurate version of the world model. The world model of a first user can be communicated (e.g., over a network such as a cloud network) to a second user so that the second user can experience the world surrounding the first user.

FIG. 6A is a block diagram of another example of a wearable system which can comprise an avatar processing and rendering system 690 in a mixed reality environment. The wearable system 600 may be part of the wearable system 200 shown in FIG. 2. In this example, the wearable system 600 can comprise a map 620, which may include at least a portion of the data in the map database 710 (shown in FIG. 7). The map may partly reside locally on the wearable system, and may partly reside at networked storage locations accessible by wired or wireless network (e.g., in a cloud system). A pose process 610 may be executed on the wearable computing architecture (e.g., processing module 260 or controller 460) and utilize data from the map 620 to determine position and orientation of the wearable computing hardware or user. Pose data may be computed from data collected on the fly as the user is experiencing the system and operating in the world. The data may comprise images, data from sensors (such as inertial measurement units, which generally comprise accelerometer and gyroscope components) and surface information pertinent to objects in the real or virtual environment.

A sparse point representation may be the output of a simultaneous localization and mapping (e.g., SLAM or vSLAM, referring to a configuration wherein the input is images/visual only) process. The system can be configured to not only find out where in the world the various components are, but what the world is made of. Pose may be a building block that achieves many goals, including populating the map and using the data from the map.

In one embodiment, a sparse point position may not be completely adequate on its own, and further information may be needed to produce a multifocal AR, VR, or MR experience. Dense representations, generally referring to depth map information, may be utilized to fill this gap at least in part. Such information may be computed from a process referred to as Stereo 640, wherein depth information is determined using a technique such as triangulation or time-of-flight sensing. Image information and active patterns (such as infrared patterns created using active projectors), images acquired from image cameras, or hand gestures/totem 650 may serve as input to the Stereo process 640. A significant amount of depth map information may be fused together, and some of this may be summarized with a surface representation. For example, mathematically definable surfaces may be efficient (e.g., relative to a large point cloud) and digestible inputs to other processing devices like game engines. Thus, the output of the stereo process (e.g., a depth map) 640 may be combined in the fusion process 630. Pose 610 may be an input to this fusion process 630 as well, and the output of fusion 630 becomes an input to populating the map process 620. Sub-surfaces may connect with each other, such as in topographical mapping, to form larger surfaces, and the map becomes a large hybrid of points and surfaces.

To resolve various aspects in a mixed reality process 660, various inputs may be utilized. For example, in the embodiment depicted in FIG. 6A, Game parameters may be inputs to determine that the user of the system is playing a monster battling game with one or more monsters at various locations, monsters dying or running away under various conditions (such as if the user shoots the monster), walls or other objects at various locations, and the like. The world map may include information regarding the location of the objects or semantic information of the objects (e.g., classifications such as whether the object is flat or round, horizontal or vertical, a table or a lamp, etc.) and the world map can be another valuable input to mixed reality. Pose relative to the world becomes an input as well and plays a key role to almost any interactive system.

Controls or inputs from the user are another input to the wearable system 600. As described herein, user inputs can include visual input, gestures, totems, audio input, sensory input, etc. In order to move around or play a game, for example, the user may need to instruct the wearable system 600 regarding what he or she wants to do. Beyond just moving oneself in space, there are various forms of user controls that may be utilized. In one embodiment, a totem (e.g. a user input device), or an object such as a toy gun may be held by the user and tracked by the system. The system preferably will be configured to know that the user is holding the item and understand what kind of interaction the user is having with the item (e.g., if the totem or object is a gun, the system may be configured to understand location and orientation, as well as whether the user is clicking a trigger or other sensed button or element which may be equipped with a sensor, such as an IMU, which may assist in determining what is going on, even when such activity is not within the field of view of any of the cameras.)

Hand gesture tracking or recognition may also provide input information. The wearable system 600 may be configured to track and interpret hand gestures for button presses, for gesturing left or right, stop, grab, hold, etc. For example, in one configuration, the user may want to flip through emails or a calendar in a non-gaming environment, or do a “fist bump” with another person or player. The wearable system 600 may be configured to leverage a minimum amount of hand gesture, which may or may not be dynamic. For example, the gestures may be simple static gestures like open hand for stop, thumbs up for ok, thumbs down for not ok; or a hand flip right, or left, or up/down for directional commands.

Eye tracking is another input (e.g., tracking where the user is looking to control the display technology to render at a specific depth or range). In one embodiment, vergence of the eyes may be determined using triangulation, and then using a vergence/accommodation model developed for that particular person, accommodation may be determined. Eye tracking can be performed by the eye camera(s) to determine eye gaze (e.g., direction or orientation of one or both eyes). Other techniques can be used for eye tracking such as, e.g., measurement of electrical potentials by electrodes placed near the eye(s) (e.g., electrooculography).

Speech tracking can be another input can be used alone or in combination with other inputs (e.g., totem tracking, eye tracking, gesture tracking, etc.). Speech tracking may include speech recognition, voice recognition, alone or in combination. The system 600 can include an audio sensor (e.g., a microphone) that receives an audio stream from the environment. The system 600 can incorporate voice recognition technology to determine who is speaking (e.g., whether the speech is from the wearer of the ARD or another person or voice (e.g., a recorded voice transmitted by a loudspeaker in the environment)) as well as speech recognition technology to determine what is being said. The local data & processing module 260 or the remote processing module 270 can process the audio data from the microphone (or audio data in another stream such as, e.g., a video stream being watched by the user) to identify content of the speech by applying various speech recognition algorithms, such as, e.g., hidden Markov models, dynamic time warping (DTW)-based speech recognitions, neural networks, deep learning algorithms such as deep feedforward and recurrent neural networks, end-to-end automatic speech recognitions, machine learning algorithms (described with reference to FIG. 7), or other algorithms that uses acoustic modeling or language modeling, etc.

The local data & processing module 260 or the remote processing module 270 can also apply voice recognition algorithms which can identify the identity of the speaker, such as whether the speaker is the user 210 of the wearable system 600 or another person with whom the user is conversing. Some example voice recognition algorithms can include frequency estimation, hidden Markov models, Gaussian mixture models, pattern matching algorithms, neural networks, matrix representation, Vector Quantization, speaker diarisation, decision trees, and dynamic time warping (DTW) technique. Voice recognition techniques can also include anti-speaker techniques, such as cohort models, and world models. Spectral features may be used in representing speaker characteristics. The local data & processing module or the remote data processing module 270 can use various machine learning algorithms described with reference to FIG. 7 to perform the voice recognition.

An implementation of a wearable system can use these user controls or inputs via a UI. UI elements (e.g., controls, popup windows, bubbles, data entry fields, etc.) can be used, for example, to dismiss a display of information, e.g., graphics or semantic information of an object.

With regard to the camera systems, the example wearable system 600 shown in FIG. 6A can include three pairs of cameras: a relative wide FOV or passive SLAM pair of cameras arranged to the sides of the user's face, a different pair of cameras oriented in front of the user to handle the stereo imaging process 640 and also to capture hand gestures and totem/object tracking in front of the user's face. The FOV cameras and the pair of cameras for the stereo process 640 may be a part of the outward-facing imaging system 464 (shown in FIG. 4). The wearable system 600 can include eye tracking cameras (which may be a part of an inward-facing imaging system 462 shown in FIG. 4) oriented toward the eyes of the user in order to triangulate eye vectors and other information. The wearable system 600 may also comprise one or more textured light projectors (such as infrared (IR) projectors) to inject texture into a scene.

The wearable system 600 can comprise an avatar processing and rendering system 690. The avatar processing and rendering system 690 can be configured to generate, update, animate, and render an avatar based on contextual information. As will be further described with reference to FIGS. 11-20, the avatar processing and rendering system 690 can use one or more control policies 695 to animate avatar movements in real time. The control policies 695 can be pre-calculated, offline from the system 695, and used in real-time to render avatar locomotion. The control policies 695 can be calculated using Markov decision process (MDP) techniques. Some or all of the avatar processing and rendering system 690 can be implemented as part of the local processing and data module 260 or the remote processing module 262, 264 alone or in combination. In various embodiments, multiple avatar processing and rendering systems 690 (e.g., as implemented on different wearable devices) can be used for rendering the virtual avatar 670. For example, a first user's wearable device may be used to determine the first user's intent, while a second user's wearable device can determine an avatar's characteristics and render the avatar of the first user based on the intent received from the first user's wearable device. The first user's wearable device and the second user's wearable device (or other such wearable devices) can communicate via a network, for example, as will be described with reference to FIGS. 9A and 9B.

FIG. 6B illustrates an example avatar processing and rendering system 690. The avatar processing and rendering system 690 can implement the mesh transfer techniques described with reference to FIGS. 11-14C. The example avatar processing and rendering system 690 can comprise a 3D model processing system 680, a contextual information analysis system 688, an avatar autoscaler 692, an intent mapping system 694, an anatomy adjustment system 698, a stimuli response system 696, alone or in combination. The system 690 is intended to illustrate functionalities for avatar processing and rendering and is not intended to be limiting. For example, in certain implementations, one or more of these systems may be part of another system. For example, portions of the contextual information analysis system 688 may be part of the avatar autoscaler 692, intent mapping system 694, stimuli response system 696, or anatomy adjustment system 698, individually or in combination.

The contextual information analysis system 688 can be configured to determine environment and object information based on one or more device sensors described with reference to FIGS. 2 and 3. For example, the contextual information analysis system 688 can analyze environments and objects (including physical or virtual objects) of a user's environment or an environment in which the user's avatar is rendered, using images acquired by the outward-facing imaging system 464 of the user or the viewer of the user's avatar. The contextual information analysis system 688 can analyze such images alone or in combination with a data acquired from location data or world maps (e.g., maps 620, 710, 910) to determine the location and layout of objects in the environments. The contextual information analysis system 688 can also access biological features of the user or human in general for animating the virtual avatar 670 realistically. For example, the contextual information analysis system 688 can generate a discomfort curve which can be applied to the avatar such that a portion of the user's avatar's body (e.g., the head) is not at an uncomfortable (or unrealistic) position with respect to the other portions of the user's body (e.g., the avatar's head is not turned 270 degrees). In certain implementations, one or more object recognizers 708 (shown in FIG. 7) may be implemented as part of the contextual information analysis system 688.

The avatar autoscaler 692, the intent mapping system 694, and the stimuli response system 696, and anatomy adjustment system 698 can be configured to determine the avatar's characteristics based on contextual information. Some example characteristics of the avatar can include the size, appearance, position, orientation, movement, pose, expression, etc. The avatar autoscaler 692 can be configured to automatically scale the avatar such that the user does not have to look at the avatar at an uncomfortable pose. For example, the avatar autoscaler 692 can increase or decrease the size of the avatar to bring the avatar to the user's eye level such that the user does not need to look down at the avatar or look up at the avatar respectively. The intent mapping system 694 can determine an intent of a user's interaction and map the intent to an avatar (rather than the exact user interaction) based on the environment that the avatar is rendered in. For example, an intent of a first user may be to communicate with a second user in a telepresence session (see, e.g., FIG. 9B). Typically, two people face each other when communicating. The intent mapping system 694 of the first user's wearable system can determine that such a face-to-face intent exists during the telepresence session and can cause the first user's wearable system to render the second user's avatar to be facing the first user. If the second user were to physically turn around, instead of rendering the second user's avatar in a turned position (which would cause the back of the second user's avatar to be rendered to the first user), the first user's intent mapping system 694 can continue to render the second avatar's face to the first user, which is the inferred intent of the telepresence session (e.g., face-to-face intent in this example).

The stimuli response system 696 can identify an object of interest in the environment and determine an avatar's response to the object of interest. For example, the stimuli response system 696 can identify a sound source in an avatar's environment and automatically turn the avatar to look at the sound source. The stimuli response system 696 can also determine a threshold termination condition. For example, the stimuli response system 696 can cause the avatar to go back to its original pose after the sound source disappears or after a period of time has elapsed.

The anatomy adjustment system 698 can be configured to adjust the user's pose based on biological features. For example, the anatomy adjustment system 698 can be configured to adjust relative positions between the user's head and the user's torso or between the user's upper body and lower body based on a discomfort curve.

The 3D model processing system 680 can be configured to animate and cause the display 220 to render a virtual avatar 670. The 3D model processing system 680 can include a virtual character processing system 682 and a movement processing system 684. The virtual character processing system 682 can be configured to generate and update a 3D model of a user (for creating and animating the virtual avatar). The movement processing system 684 can be configured to animate the avatar, such as, e.g., by changing the avatar's pose, by moving the avatar around in a user's environment, or by animating the avatar's facial expressions, etc. As will further be described herein, the virtual avatar can be animated using rigging techniques. In some embodiments, an avatar is represented in two parts: a surface representation (e.g., a deformable mesh) that is used to render the outward appearance of the virtual avatar and a hierarchical set of interconnected joints (e.g., a core skeleton) for animating the mesh. In some implementations, the virtual character processing system 682 can be configured to edit or generate surface representations, while the movement processing system 684 can be used to animate the avatar by moving the avatar, deforming the mesh, etc. For example, in some implementations, the 3D model processing system 680 (e.g., via the character processing system 682) can access polygon strip (poly strip) data for hair on an avatar and render the poly strip hair for display to the user.

Examples of Mapping a User's Environment

FIG. 7 is a block diagram of an example of an MR environment 700.

The MR environment 700 may be configured to receive input (e.g., visual input 702 from the user's wearable system, stationary input 704 such as room cameras, sensory input 706 from various sensors, gestures, totems, eye tracking, user input from the user input device 466 etc.) from one or more user wearable systems (e.g., wearable system 200 or display system 220) or stationary room systems (e.g., room cameras, etc.). The wearable systems can use various sensors (e.g., accelerometers, gyroscopes, temperature sensors, movement sensors, depth sensors, GPS sensors, inward-facing imaging system, outward-facing imaging system, etc.) to determine the location and various other attributes of the environment of the user. This information may further be supplemented with information from stationary cameras in the room that may provide images or various cues from a different point of view. The image data acquired by the cameras (such as the room cameras and/or the cameras of the outward-facing imaging system) may be reduced to a set of mapping points.

One or more object recognizers 708 can crawl through the received data (e.g., the collection of points) and recognize or map points, tag images, attach semantic information to objects with the help of a map database 710. The map database 710 may comprise various points collected over time and their corresponding objects. The various devices and the map database can be connected to each other through a network (e.g., LAN, WAN, etc.) to access the cloud.

Based on this information and collection of points in the map database, the object recognizers 708 a to 708 n may recognize objects in an environment. For example, the object recognizers can recognize faces, persons, windows, walls, doors, user input devices, televisions, documents, other objects in the user's environment, etc. One or more object recognizers may be specialized for object with certain characteristics. For example, the object recognizer 708 a may be used to recognizer faces, while another object recognizer may be used recognize cuboidal objects (e.g., desks, chairs, sofas, tables, etc.).

The object recognitions may be performed using a variety of computer vision techniques. For example, the wearable system can analyze the images acquired by the outward-facing imaging system 464 (shown in FIG. 4) to perform scene reconstruction, event detection, video tracking, object recognition (e.g., persons, objects, or documents), object pose estimation, facial recognition (e.g., from a person in the environment or an image on a document), learning, indexing, motion estimation, or image analysis (e.g., identifying indicia within documents such as photos, signatures, identification information, travel information, etc.), and so forth. One or more computer vision algorithms may be used to perform these tasks. Non-limiting examples of computer vision algorithms include: Scale-invariant feature transform (SIFT), speeded up robust features (SURF), oriented FAST and rotated BRIEF (ORB), binary robust invariant scalable keypoints (BRISK), fast retina keypoint (FREAK), Viola-Jones algorithm, Eigenfaces approach, Lucas-Kanade algorithm, Horn-Schunk algorithm, Mean-shift algorithm, visual simultaneous location and mapping (vSLAM) techniques, a sequential Bayesian estimator (e.g., Kalman filter, extended Kalman filter, etc.), bundle adjustment, Adaptive thresholding (and other thresholding techniques), Iterative Closest Point (ICP), Semi Global Matching (SGM), Semi Global Block Matching (SGBM), Feature Point Histograms, various machine learning algorithms (such as e.g., support vector machine, k-nearest neighbors algorithm, Naive Bayes, neural network (including convolutional or deep neural networks), or other supervised/unsupervised models, etc.), and so forth.

The object recognitions can additionally or alternatively be performed by a variety of machine learning algorithms. Once trained, the machine learning algorithm can be stored by the HMD. Some examples of machine learning algorithms can include supervised or non-supervised machine learning algorithms, including regression algorithms (such as, for example, Ordinary Least Squares Regression), instance-based algorithms (such as, for example, Learning Vector Quantization), decision tree algorithms (such as, for example, classification and regression trees), Bayesian algorithms (such as, for example, Naive Bayes), clustering algorithms (such as, for example, k-means clustering), association rule learning algorithms (such as, for example, a-priori algorithms), artificial neural network algorithms (such as, for example, Perceptron), deep learning algorithms (such as, for example, Deep Boltzmann Machine, or deep neural network), dimensionality reduction algorithms (such as, for example, Principal Component Analysis), ensemble algorithms (such as, for example, Stacked Generalization), and/or other machine learning algorithms. In some embodiments, individual models can be customized for individual data sets. For example, the wearable device can generate or store a base model. The base model may be used as a starting point to generate additional models specific to a data type (e.g., a particular user in the telepresence session), a data set (e.g., a set of additional images obtained of the user in the telepresence session), conditional situations, or other variations. In some embodiments, the wearable HMD can be configured to utilize a plurality of techniques to generate models for analysis of the aggregated data. Other techniques may include using pre-defined thresholds or data values.

Based on this information and collection of points in the map database, the object recognizers 708 a to 708 n may recognize objects and supplement objects with semantic information to give life to the objects. For example, if the object recognizer recognizes a set of points to be a door, the system may attach some semantic information (e.g., the door has a hinge and has a 90 degree movement about the hinge). If the object recognizer recognizes a set of points to be a mirror, the system may attach semantic information that the mirror has a reflective surface that can reflect images of objects in the room. The semantic information can include affordances of the objects as described herein. For example, the semantic information may include a normal of the object. The system can assign a vector whose direction indicates the normal of the object. Over time the map database grows as the system (which may reside locally or may be accessible through a wireless network) accumulates more data from the world. Once the objects are recognized, the information may be transmitted to one or more wearable systems. For example, the MR environment 700 may include information about a scene happening in California. The environment 700 may be transmitted to one or more users in New York. Based on data received from an FOV camera and other inputs, the object recognizers and other software components can map the points collected from the various images, recognize objects etc., such that the scene may be accurately “passed over” to a second user, who may be in a different part of the world. The environment 700 may also use a topological map for localization purposes.

FIG. 8 is a process flow diagram of an example of a method 800 of rendering virtual content in relation to recognized objects. The method 800 describes how a virtual scene may be presented to a user of the wearable system. The user may be geographically remote from the scene. For example, the user may be in New York, but may want to view a scene that is presently going on in California, or may want to go on a walk with a friend who resides in California.

At block 810, the wearable system may receive input from the user and other users regarding the environment of the user. This may be achieved through various input devices, and knowledge already possessed in the map database. The user's FOV camera, sensors, GPS, eye tracking, etc., convey information to the system at block 810. The system may determine sparse points based on this information at block 820. The sparse points may be used in determining pose data (e.g., head pose, eye pose, body pose, or hand gestures) that can be used in displaying and understanding the orientation and position of various objects in the user's surroundings. The object recognizers 708 a-708 n may crawl through these collected points and recognize one or more objects using a map database at block 830. This information may then be conveyed to the user's individual wearable system at block 840, and the desired virtual scene may be accordingly displayed to the user at block 850. For example, the desired virtual scene (e.g., user in CA) may be displayed at the appropriate orientation, position, etc., in relation to the various objects and other surroundings of the user in New York.

Example Communications Among Multiple Wearable Systems

FIG. 9A schematically illustrates an overall system view depicting multiple user devices interacting with each other. The computing environment 900 includes user devices 930 a, 930 b, 930 c. The user devices 930 a, 930 b, and 930 c can communicate with each other through a network 990. The user devices 930 a-930 c can each include a network interface to communicate via the network 990 with a remote computing system 920 (which may also include a network interface 971). The network 990 may be a LAN, WAN, peer-to-peer network, radio, Bluetooth, or any other network. The computing environment 900 can also include one or more remote computing systems 920. The remote computing system 920 may include server computer systems that are clustered and located at different geographic locations. The user devices 930 a, 930 b, and 930 c may communicate with the remote computing system 920 via the network 990.

The remote computing system 920 may include a remote data repository 980 which can maintain information about a specific user's physical and/or virtual worlds. Data storage 980 can store information related to users, users' environment (e.g., world maps of the user's environment), or configurations of avatars of the users. The remote data repository may be an embodiment of the remote data repository 280 shown in FIG. 2. The remote computing system 920 may also include a remote processing module 970. The remote processing module 970 may be an embodiment of the remote processing module 270 shown in FIG. 2. The remote processing module 970 may include one or more processors which can communicate with the user devices (930 a, 930 b, 930 c) and the remote data repository 980. The processors can process information obtained from user devices and other sources. In some implementations, at least a portion of the processing or storage can be provided by the local processing and data module 260 (as shown in FIG. 2). The remote computing system 920 may enable a given user to share information about the specific user's own physical and/or virtual worlds with another user.

The user device may be a wearable device (such as an HMD or an ARD), a computer, a mobile device, or any other devices alone or in combination. For example, the user devices 930 b and 930 c may be an embodiment of the wearable system 200 shown in FIG. 2 (or the wearable system 400 shown in FIG. 4) which can be configured to present AR/VR/MR content.

One or more of the user devices can be used with the user input device 466 shown in FIG. 4. A user device can obtain information about the user and the user's environment (e.g., using the outward-facing imaging system 464 shown in FIG. 4). The user device and/or remote computing system 1220 can construct, update, and build a collection of images, points and other information using the information obtained from the user devices. For example, the user device may process raw information acquired and send the processed information to the remote computing system 1220 for further processing. The user device may also send the raw information to the remote computing system 1220 for processing. The user device may receive the processed information from the remote computing system 1220 and provide final processing before projecting to the user. The user device may also process the information obtained and pass the processed information to other user devices. The user device may communicate with the remote data repository 1280 while processing acquired information. Multiple user devices and/or multiple server computer systems may participate in the construction and/or processing of acquired images.

The information on the physical worlds may be developed over time and may be based on the information collected by different user devices. Models of virtual worlds may also be developed over time and be based on the inputs of different users. Such information and models can sometimes be referred to herein as a world map or a world model. As described with reference to FIGS. 6 and 7, information acquired by the user devices may be used to construct a world map 910. The world map 910 may include at least a portion of the map 620 described in FIG. 6A. Various object recognizers (e.g. 708 a, 708 b, 708 c . . . 708 n) may be used to recognize objects and tag images, as well as to attach semantic information to the objects. These object recognizers are also described in FIG. 7.

The remote data repository 980 can be used to store data and to facilitate the construction of the world map 910. The user device can constantly update information about the user's environment and receive information about the world map 910. The world map 910 may be created by the user or by someone else. As discussed herein, user devices (e.g. 930 a, 930 b, 930 c) and remote computing system 920, alone or in combination, may construct and/or update the world map 910. For example, a user device may be in communication with the remote processing module 970 and the remote data repository 980. The user device may acquire and/or process information about the user and the user's environment. The remote processing module 970 may be in communication with the remote data repository 980 and user devices (e.g. 930 a, 930 b, 930 c) to process information about the user and the user's environment. The remote computing system 920 can modify the information acquired by the user devices (e.g. 930 a, 930 b, 930 c), such as, e.g. selectively cropping a user's image, modifying the user's background, adding virtual objects to the user's environment, annotating a user's speech with auxiliary information, etc. The remote computing system 920 can send the processed information to the same and/or different user devices.

Examples of a Telepresence Session

FIG. 9B depicts an example where two users of respective wearable systems are conducting a telepresence session. Two users (named Alice 912 and Bob 914 in this example) are shown in this figure. The two users are wearing their respective wearable devices 902 and 904 which can include an HMD described with reference to FIG. 2 (e.g., the display device 220 of the system 200) for representing a virtual avatar of the other user in the telepresence session. The two users can conduct a telepresence session using the wearable device. Note that the vertical line in FIG. 9B separating the two users is intended to illustrate that Alice 912 and Bob 914 may (but need not) be in two different locations while they communicate via telepresence (e.g., Alice may be inside her office in Atlanta while Bob is outdoors in Boston).

As described with reference to FIG. 9A, the wearable devices 902 and 904 may be in communication with each other or with other user devices and computer systems. For example, Alice's wearable device 902 may be in communication with Bob's wearable device 904, e.g., via the network 990 (shown in FIG. 9A). The wearable devices 902 and 904 can track the users' environments and movements in the environments (e.g., via the respective outward-facing imaging system 464, or one or more location sensors) and speech (e.g., via the respective audio sensor 232). The wearable devices 902 and 904 can also track the users' eye movements or gaze based on data acquired by the inward-facing imaging system 462. In some situations, the wearable device can also capture or track a user's facial expressions or other body movements (e.g., arm or leg movements) where a user is near a reflective surface and the outward-facing imaging system 464 can obtain reflected images of the user to observe the user's facial expressions or other body movements.

A wearable device can use information acquired of a first user and the environment to animate a virtual avatar that will be rendered by a second user's wearable device to create a tangible sense of presence of the first user in the second user's environment. For example, the wearable devices 902 and 904, the remote computing system 920, alone or in combination, may process Alice's images or movements for presentation by Bob's wearable device 904 or may process Bob's images or movements for presentation by Alice's wearable device 902. As further described herein, the avatars can be rendered based on contextual information such as, e.g., a user's intent, an environment of the user or an environment in which the avatar is rendered, or other biological features of a human.

Although the examples only refer to two users, the techniques described herein should not be limited to two users. Multiple users (e.g., two, three, four, five, six, or more) using wearables (or other telepresence devices) may participate in a telepresence session. A particular user's wearable device can present to that particular user the avatars of the other users during the telepresence session. Further, while the examples in this figure show users as standing in an environment, the users are not required to stand. Any of the users may stand, sit, kneel, lie down, walk or run, or be in any position or movement during a telepresence session. The user may also be in a physical environment other than described in examples herein. The users may be in separate environments or may be in the same environment while conducting the telepresence session. Not all users are required to wear their respective HMDs in the telepresence session. For example, Alice 912 may use other image acquisition and display devices such as a webcam and computer screen while Bob 914 wears the wearable device 904.

Examples of a Virtual Avatar

FIG. 10 illustrates an example of an avatar as perceived by a user of a wearable system. The example avatar 1000 shown in FIG. 10 can be an avatar of Alice 912 (shown in FIG. 9B) standing behind a physical plant in a room. An avatar can include various characteristics, such as for example, size, appearance (e.g., skin color, complexion, hair, hair style, clothes, facial features, such as wrinkles, moles, blemishes, pimples, dimples, etc.), position, orientation, movement, pose, expression, etc. These characteristics may be based on the user associated with the avatar (e.g., the avatar 1000 of Alice may have some or all characteristics of the actual person Alice 912). As described herein, hair 1020 of the avatar 1000 can be represented as poly strip hair and rendered in real-time to realistically show the behavior of the avatar's hair. In some implementations, the poly strip hair of the avatar 1000 is generated based at least partly on previously-generated, high-fidelity strand-based hair for the avatar. As further described herein, the avatar 1000 can be animated based on contextual information, which can include adjustments to one or more of the characteristics of the avatar 1000. Although generally described herein as representing the physical appearance of the person (e.g., Alice), this is for illustration and not limitation. Alice's avatar could represent the appearance of another real or fictional human being besides Alice, a personified object, a creature, or any other real or fictitious representation. Further, the plant in FIG. 10 need not be physical, but could be a virtual representation of a plant that is presented to the user by the wearable system. Also, additional or different virtual content than shown in FIG. 10 could be presented to the user.

Examples of Rigging Systems for Virtual Characters

An animated virtual character, such as a human avatar, can be wholly or partially represented in computer graphics as a polygon mesh. A polygon mesh, or simply “mesh” for short, is a collection of points in a modeled three-dimensional space. The mesh can form a polyhedral object whose surfaces define the body or shape of the virtual character (or a portion thereof). While meshes can include any number of points (within practical limits which may be imposed by available computing power), finer meshes with more points are generally able to portray more realistic virtual characters with finer details that may closely approximate real life people, animals, objects, etc. FIG. 10 shows an example of a mesh 1010 around an eye of the avatar 1000. While meshes can include any number of points (within practical limits which may be imposed by available computing power), finer meshes with more points are generally able to portray more realistic virtual characters with finer details that may closely approximate real life people, animals, objects, etc. FIG. 10 shows an example of a mesh 1010 around an eye of the avatar 1000. The mesh 1010 may be relatively fine (e.g., have a relatively large number of points), to facilitate a high quality simulation of the complex curves and movements that occur in the eye region.

Each point in the mesh can be defined by a coordinate in the modeled three-dimensional space. The modeled three-dimensional space can be, for example, a Cartesian space addressed by (x, y, z) coordinates. The points in the mesh are the vertices of the polygons which make up the polyhedral object. Each polygon represents a surface, or face, of the polyhedral object and is defined by an ordered set of vertices, with the sides of each polygon being straight line edges connecting the ordered set of vertices. In some cases, the polygon vertices in a mesh may differ from geometric polygons in that they are not necessarily coplanar in 3D graphics. In addition, the vertices of a polygon in a mesh may be collinear, in which case the polygon has zero area (referred to as a degenerate polygon).

In some embodiments, a mesh is made up of three-vertex polygons (i.e., triangles or “tris” for short) or four-vertex polygons (i.e., quadrilaterals or “quads” for short). However, higher-order polygons can also be used in some meshes. Meshes are typically quad-based in direct content creation (DCC) applications (e.g., applications such as Maya (available from Autodesk, Inc.) or Houdini (available from Side Effects Software Inc.) which are primarily designed for creating and manipulating 3D computer graphics), whereas meshes are typically tri-based in real-time applications.

To animate a virtual character, its mesh can be deformed by moving some or all of its vertices to new positions in space at various instants in time. The deformations can represent both large-scale movements (e.g., movement of limbs) and fine movements (e.g., facial movements). These and other deformations can be based on real-world models (e.g., photogrammetric scans of real humans performing body movements, articulations, facial contortions, expressions, etc.), art-directed development (which may be based on real-world sampling), combinations of the same, or other techniques. In the early days of computer graphics, mesh deformations could be accomplished manually by independently setting new positions for the vertices, but given the size and complexity of modem meshes it is typically desirable to produce deformations using automated systems and processes. The control systems, processes, and techniques for producing these deformations are referred to as rigging, or simply “the rig.” The example avatar processing and rendering system 690 of FIG. 6B includes a 3D model processing system 680 which can implement rigging.

The rigging for a virtual character can use skeletal systems to assist with mesh deformations. A skeletal system includes a collection of joints which correspond to points of articulation for the mesh. In the context of rigging, joints are sometimes also referred to as “bones” despite the difference between these terms when used in the anatomical sense. Joints in a skeletal system can move, or otherwise change, with respect to one another according to transforms which can be applied to the joints. The transforms can include translations or rotations in space, as well as other operations. The joints can be assigned hierarchical relationships (e.g., parent-child relationships) with respect to one another. These hierarchical relationships can allow one joint to inherit transforms or other characteristics from another joint. For example, a child joint in a skeletal system can inherit a transform assigned to its parent joint so as to cause the child joint to move together with the parent joint.

A skeletal system for a virtual character can be defined with joints at appropriate positions, and with appropriate local axes of rotation, degrees of freedom, etc., to allow for a desired set of mesh deformations to be carried out. Once a skeletal system has been defined for a virtual character, each joint can be assigned, in a process called “skinning,” an amount of influence over the various vertices in the mesh. This can be done by assigning a weight value to each vertex for each joint in the skeletal system. When a transform is applied to any given joint, the vertices under its influence can be moved, or otherwise altered, automatically based on that joint transform by amounts which can be dependent upon their respective weight values.

A rig can include multiple skeletal systems. One type of skeletal system is a core skeleton (also referred to as a low-order skeleton) which can be used to control large-scale movements of the virtual character. In the case of a human avatar, for example, the core skeleton might resemble the anatomical skeleton of a human. Although the core skeleton for rigging purposes may not map exactly to an anatomically-correct skeleton, it may have a sub-set of joints in analogous locations with analogous orientations and movement properties.

As briefly mentioned above, a skeletal system of joints can be hierarchical with, for example, parent-child relationships among joints. When a transform (e.g., a change in position and/or orientation) is applied to a particular joint in the skeletal system, the same transform can be applied to all other lower-level joints within the same hierarchy. In the case of a rig for a human avatar, for example, the core skeleton may include separate joints for the avatar's shoulder, elbow, and wrist. Among these, the shoulder joint may be assigned to the highest level in the hierarchy, while the elbow joint can be assigned as a child of the shoulder joint, and the wrist joint can be assigned as a child of the elbow joint. Accordingly, when a particular translation and/or rotation transform is applied to the shoulder joint, the same transform can also be applied to the elbow joint and the wrist joint such that they are translated and/or rotated in the same way as the shoulder.

Despite the connotations of its name, a skeletal system in a rig need not necessarily represent an anatomical skeleton. In rigging, skeletal systems can represent a wide variety of hierarchies used to control deformations of the mesh. For example, hair can be represented as a series of joints in a hierarchical chain; skin motions due to an avatar's facial contortions (which may represent expressions such as smiling, frowning, laughing, speaking, blinking, etc.) can be represented by a series of facial joints controlled by a facial rig; muscle deformation can be modeled by joints; and motion of clothing can be represented by a grid of joints.

The rig for a virtual character can include multiple skeletal systems, some of which may drive the movement of others. A lower-order skeletal system is one which drives one or more higher-order skeletal systems. Conversely, higher-order skeletal systems are ones which are driven or controlled by a lower-order skeletal system. For example, whereas the movements of the core skeleton of a character might be controlled manually by an animator, the core skeleton can in turn drive or control the movements of a higher-order skeletal system. For example, higher-order helper joints—which may not have anatomical analogs in a physical skeleton—can be provided to improve the mesh deformations which result from movements of the core skeleton. The transforms applied to these and other joints in higher-order skeletal systems may be derived algorithmically from the transforms applied to the lower-order skeleton. Higher-order skeletons can represent, for example, muscles, skin, fat, clothing, hair, or any other skeletal system which does not require direct animation control.

As already discussed, transforms can be applied to joints in skeletal systems in order to carry out mesh deformations. In the context of rigging, transforms include functions which accept one or more given points in 3D space and produce an output of one or more new 3D points. For example, a transform can accept one or more 3D points which define a joint and can output one or more new 3D points which specify the transformed joint. Joint transforms can include, for example, a translation component, a rotation component, and a scale component.

A translation is a transform which moves a set of one or more specified points in the modeled 3D space by a specified amount with no change in the orientation or size of the set of points. A rotation is a transform which rotates a set of one or more specified points in the modeled 3D space about a specified axis by a specified amount (e.g., rotate every point in the mesh 45 degrees about the z-axis). An affine transform (or 6 degree of freedom (DOF) transform) is one which only includes translation(s) and rotation(s). Application of an affine transform can be thought of as moving a set of one or more points in space without changing its size, though the orientation can change.

Meanwhile, a scale transform is one which modifies one or more specified points in the modeled 3D space by scaling their respective coordinates by a specified value. This changes the size and/or shape of the transformed set of points. A uniform scale transform scales each coordinate by the same amount, whereas a non-uniform scale transform can scale the (x, y, z) coordinates of the specified points independently. A non-uniform scale transform can be used, for example, to provide squashing and stretching effects, such as those which may result from muscular action. Yet another type of transform is a shear transform. A shear transform is one which modifies a set of one or more specified points in the modeled 3D space by translating a coordinate of the points by different amounts based on the distance of that coordinate from an axis.

When a transform is applied to a joint to cause it to move, the vertices under the influence of that joint are also moved. This results in deformations of the mesh. As discussed above, the process of assigning weights to quantify the influence each joint has over each vertex is called skinning (or sometimes “weight painting” or “skin weighting”). The weights are typically values between 0 (meaning no influence) and 1 (meaning complete influence). Some vertices in the mesh may be influenced only by a single joint. In that case those vertices are assigned weight values of 1 for that joint, and their positions are changed based on transforms assigned to that specific joint but no others. Other vertices in the mesh may be influenced by multiple joints. In that case, separate weights are assigned to those vertices for all of the influencing joints, with the sum of the weights for each vertex equaling 1. The positions of these vertices are changed based on transforms assigned to all of their influencing joints.

Making weight assignments for all of the vertices in a mesh can be extremely labor intensive, especially as the number of joints increases. Balancing the weights to achieve desired mesh deformations in response to transforms applied to the joints can be quite difficult for even highly trained artists. In the case of real-time applications, the task can be complicated further by the fact that many real-time systems also enforce limits on the number of joints (generally 8 or fewer) which can be weighted to a specific vertex. Such limits are typically imposed for the sake of efficiency in the graphics processing unit (GPU).

The term skinning also refers to the process of actually deforming the mesh, using the assigned weights, based on transforms applied to the joints in a skeletal system. For example, a series of core skeleton joint transforms may be specified by an animator to produce a desired character movement (e.g., a running movement or a dance step). When transforms are applied to one or more of the joints, new positions are calculated for the vertices under the influence of the transformed joints. The new position for any given vertex is typically computed as a weighted average of all the joint transforms which influence that particular vertex. There are many algorithms used for computing this weighted average, but the most common, and the one used in most real-time applications due to its simplicity and ease of control, is linear blend skinning (LBS). In linear blend skinning, a new position for each vertex is calculated using each joint transform for which that vertex has a non-zero weight. Then, the new vertex coordinates resulting from each of these joint transforms are summed in proportion to the respective weights assigned to that vertex for each of the joints. There are well known limitations to LBS in practice, and much of the work in making high-quality rigs (e.g., for movies) is devoted to finding and overcoming these limitations. Many helper joint systems are designed specifically for this purpose.

In addition to skeletal systems, “blendshapes” can also be used in rigging to produce mesh deformations. A blendshape (sometimes also called a “morph target” or just a “shape”) is a deformation applied to a set of vertices in the mesh where each vertex in the set is moved a specified amount in a specified direction based upon a weight. Each vertex in the set may have its own custom motion for a specific blendshape, and moving the vertices in the set simultaneously will generate the desired shape. The custom motion for each vertex in a blendshape can be specified by a “delta,” which is a vector representing the amount and direction of XYZ motion applied to that vertex. Blendshapes can be used to produce, for example, facial deformations to move the eyes, lips, brows, nose, dimples, etc., just to name a few possibilities.

Blendshapes are useful for deforming the mesh in an art-directable way. They offer a great deal of control, as the exact shape can be sculpted or captured from a scan of a model. But the benefits of blendshapes come at the cost of having to store the deltas for all the vertices in the blendshape. For animated characters with fine meshes and many blendshapes, the amount of delta data can be significant.

Each blendshape can be applied to a specified degree by using blendshape weights. These weights typically range from 0 (where the blendshape is not applied at all) to 1 (where the blendshape is fully active). For example, a blendshape to move a character's eyes can be applied with a small weight to move the eyes a small amount, or it can be applied with a large weight to create a larger eye movement.

The rig may apply multiple blendshapes in combinations with one another to achieve a desired complex deformation. For example, to produce a smile, the rig may apply blendshapes for lip corner pull, raising the upper lip, and lowering the lower lip, as well as moving the eyes, brows, nose, and dimples. The desired shape from combining two or more blendshapes is known as a combination shape (or simply a “combo”).

One problem that can result from applying two blendshapes in combination is that the blendshapes may operate on some of the same vertices. When both blendshapes are active, the result is called a double transform or “going off-model.” The solution to this is typically a corrective blendshape. A corrective blendshape is a special blendshape which represents a desired deformation with respect to a currently applied deformation rather than representing a desired deformation with respect to the neutral. Corrective blendshapes (or just “correctives”) can be applied based upon the weights of the blendshapes they are correcting. For example, the weight for the corrective blendshape can be made proportionate to the weights of the underlying blendshapes which trigger application of the corrective blendshape.

Corrective blendshapes can also be used to correct skinning anomalies or to improve the quality of a deformation. For example, a joint may represent the motion of a specific muscle, but as a single transform it cannot represent all the non-linear behaviors of the skin, fat, and muscle. Applying a corrective, or a series of correctives, as the muscle activates can result in more pleasing and convincing deformations.

Rigs are built in layers, with lower, simpler layers often driving higher-order layers. This applies to both skeletal systems and blendshape deformations. For example, as already mentioned, the rigging for an animated virtual character may include higher-order skeletal systems which are controlled by lower-order skeletal systems. There are many ways to control a higher-order skeleton or a blendshape based upon a lower-order skeleton, including constraints, logic systems, and pose-based deformation.

A constraint is typically a system where a particular object or joint transform controls one or more components of a transform applied to another joint or object. There are many different types of constraints. For example, aim constraints change the rotation of the target transform to point in specific directions or at specific objects. Parent constraints act as virtual parent-child relationships between pairs of transforms. Position constraints constrain a transform to specific points or a specific object. Orientation constraints constrain a transform to a specific rotation of an object. Additional discussion of constraints that can be used in embodiments of the disclosed joint decompositions is provided below.

Logic systems are systems of mathematical equations which produce some outputs given a set of inputs. These are specified, not learned. For example, a blendshape value might be defined as the product of two other blendshapes (this is an example of a corrective shape known as a combination or combo shape).

Pose-based deformations can also be used to control higher-order skeletal systems or blendshapes. The pose of a skeletal system is defined by the collection of transforms (e.g., rotation(s) and translation(s)) for all the joints in that skeletal system. Poses can also be defined for subsets of the joints in a skeletal system. For example, an arm pose could be defined by the transforms applied to the shoulder, elbow, and wrist joints. A pose space deformer (PSD) is a system used to determine a deformation output for a particular pose based on one or more “distances” between that pose and a defined pose. These distances can be metrics which characterize how different one of the poses is from the other. A PSD can include a pose interpolation node which, for example, accepts a set of joint rotations (defining a pose) as input parameters and in turn outputs normalized per-pose weights to drive a deformer, such as a blendshape. The pose interpolation node can be implemented in a variety of ways, including with radial basis functions (RBFs). RBFs can perform a machine-learned mathematical approximation of a function. RBFs can be trained using a set of inputs and their associated expected outputs. The training data could be, for example, multiple sets of joint transforms (which define particular poses) and the corresponding blendshapes (or linear skins) to be applied in response to those poses. Once the function is learned, new inputs (e.g., poses) can be given and their expected outputs can be computed efficiently. RBFs are a subtype of artificial neural networks. RBFs can be used to drive higher-level components of a rig based upon the state of lower-level components. For example, the pose of a core skeleton can drive helper joints and correctives at higher levels.

These control systems can be chained together to perform complex behaviors. As an example, an eye rig could contain two “look around” values for horizontal and vertical rotation. These values can be passed through some logic to determine the exact rotation of an eye joint transform, which might in turn be used as an input to an RBF which controls blendshapes that change the shape of the eyelid to match the position of the eye. The activation values of these shapes might be used to drive other components of a facial expression using additional logic, and so on.

A goal of rigging systems is typically to provide a mechanism to produce pleasing, high-fidelity deformations based on simple, human-understandable control systems. In the case of real-time applications, the goal is typically to provide rigging systems which are simple enough to run in real-time on, for example, a VR/AR/MR system 200, while making as few compromises to the final quality as possible. In some embodiments, the 3D model processing system 680 executes a rigging system to animate an avatar in a mixed reality environment 100 in real-time to be interactive (with users of the VR/AR/MR system) and to provide appropriate, contextual avatar behavior (e.g., intent-based behavior) in the user's environment.

Example Generation of Strand-Based Hair for a Virtual Avatar

Creation of a virtual avatar can be a time-consuming and intensive process, which requires much interaction with human animators and much trial-and-error to achieve a realistic, high-fidelity digital character. In some implementations, the high-fidelity avatar is generated offline from a real-time rendering system (e.g., the wearable system 200 in an ARNR/MR context). The high-fidelity digital avatar data can be reduced to a lower-resolution format (e.g., via decimation) that is suitable for the real-time rendering system.

The hair for the virtual avatar can be created as strand-based hair, which can comprise hundreds of thousands of individual, digital, virtual strands that are designed to mimic real hair strands.

FIGS. 11A-11E are examples that show some of the processes in generating strand-based hair for a virtual avatar 1000. The process can be performed by a computing system comprising computer hardware and non-transitory storage such as, e.g., the remote computing system 920, which executes a 3D animation application. A human animator may direct or control the generation of the avatar's strand-based hair using the 3D animation application such as, e.g., Maya (available from Autodesk Inc., San Rafael, Calif.), Blender (available at www.blender.org), or Houdini (available from Side Effects Software Inc., Toronto, Canada).

FIG. 11A is an example of the head of an avatar 1000 to which strand-based hair is to be attached. Guide curves 1110 determine the overall hair shape, and an animator models these guide curves using the 3D animation software application. Guide curves are also sometimes referred to as guide hairs, control hairs, or guide strands. FIG. 11B is a close-up view of a portion (shown in a dot-dash box) of the head of the avatar 1000 shown in FIG. 11A showing the guide curves 1110, with one of the guide curves 1110 a highlighted. The guide curves 1110 are 3D curves having an end at the scalp (sometimes referred to as the root or the base), extending to a tip, and each curve can be individually shaped by the animator. Points 1111 on or adjacent the guide curve 1110 a are control points, which the animator can move to shape the curvature and position of the guide curve.

Guide curves can be located throughout the hair region and are not necessarily evenly distributed over the surface (e.g., the scalp). Since the final, strand-based hair may include over 100,000 strands, it is impractical for the animator to model each individual strand. The animator models each guide curve 1110 (typically several hundred) by adjusting its shape, properties, spacing, etc., and the 3D animation application interpolates the additional hair strands across the hair area as instances of the guide curves. Thus, strand hair is sometimes referred to as instanced hair (or interpolated hair), because the hair strands are instances of the modeled guide curves. In the description below, the terms guide curves or strands (or hair strands) may be used interchangeably, since strands are instances of the guide curves, and the poly strip hair generation techniques can operate with guide curves, strands, or both guide curves and strands.

The guide curves or strands can be modeled as a series of points (or vertices) ordered from the root to the tip, and can be associated with a number of strand parameters that define the characteristics of the guide curve (e.g., how points of each guide curve are connected to each other). As noted above, additional strands of hair can be generated as instances of the guide curves to provide a full set of hair for the avatar. The instances of the hair can be generated from the guide curves based at least partly on hair instance parameters. For example, hair instance parameters can include curliness, clumpiness (which breaks up the uniformity of hair flow), taper, width, distribution across the surface (e.g., uniform or clumped), length, color, noise (which generates small waves through the strands), density (e.g., how many strands (or strands per unit area) will be instanced), etc.

FIG. 11C illustrates an example of a hair mask 1120 on the head of the avatar 1000. The avatar 1000 is shown in 3D wireframe on the left side of FIG. 11C, with the hair mask 1120 superimposed on the avatar 1000. The hair mask 1120 is the region of the avatar on which hair will be generated and is shown in the wireframe mesh as mesh cells with central dots. The hair mask 1120 can be specific to a particular avatar to show the individual nature of where the avatar's hair grows on the avatar's head. The hair mask for an avatar representing a human model can be obtained from one or more images of the model (e.g., via photogrammetry). FIG. 11C also shows the guide curves 1110.

FIG. 11C includes an example UV view 1130 of the avatar 1000 and the hair mask 1120 in texture coordinate space. The UV view 1130 shows a UV mask (also called a map) of the front and the back of the avatar's head with the hair mask 1120 illustrated with cross-hatching. The hair mask is sometimes referred to as a UV mask or UV map.

UV mapping is a 3D modeling process of projecting a 2D image to the surface of a 3D object (e.g., an avatar) for texture mapping. UV mapping projects a texture map onto the 3D object to add texture to the polygons defining the 3D object. The letters U and V are commonly used to denote the (orthogonal) axes of the 2D texture space, because the letters x, y, and z are commonly used to denote the Cartesian axes of 3D physical space. Each of the coordinates U and V typically is in the range from 0 to 1. UV texturing permits polygons that make up the 3D object (e.g., the polygons are shown on the 3D wireframe of the avatar 1000 on the left in FIG. 11C) to be applied with texture (e.g., color, high frequency details, surface texture, surface attributes) from a UV texture map image. The UV mapping process assigns pixels in the texture map image to surface mappings on polygons of the 3D object. The texture image can comprise a 2D image (e.g., a bitmap with, for example, 0 to 255 values per pixel). For the hair generation techniques described herein, the texture images are for different styles of relatively short tufts or clumps of hair (see, e.g., the example hair textures shown in FIG. 16A).

Hairlines and parts can be represented in the hair mask 1120. For example, as shown in FIG. 11C, an edge of the hair mask 1120 can represent a hairline 1140. FIG. 11D (3D view on the left, UV view 1130 on the right) shows an example of a part 1150 in the hair mask 1120. The hair in the hair region 1155 of the hair mask 1120 (e.g., on the left side of the avatar's head) can have different characteristics than the hair on the other side of the part 1150 (e.g., the direction in which the hair lays on the avatar's head). Hairlines 1140 and parts 1150 can be automatically detected by the 3D animation application, e.g., via contour detection (which can provide an outline of the hairline(s) and part(s)).

FIG. 11E is a rendering of an example of strand-based hair 1170 on the avatar 1000. Hair strands are instanced (by the 3D animation application) based on the guide curves 1110, the hair mask 1120, the hair regions 1155 (e.g., representing parts), and the hair instance parameters (e.g., density, distribution, clumping, curliness, noise, etc.).

Although very lifelike in appearance, strand-based hair cannot readily be converted to a lower-resolution format that is suitable for real-time rendering applications (e.g., in AR/MR/VR or gaming). Conversion to the lower-resolution format typically requires many iterations and much manual input by a human animator to make the hair look realistic. Such lower-resolution conversion high-fidelity strand-based hair may not be practical when hair for many different avatars needs to be generated.

Accordingly, embodiments of the systems and methods described herein can utilize strand-based hair generated for an avatar as an input to generate polygon strip (poly strip) hair for the avatar. The poly strip hair generation process can be automated and performed with relatively little animator input. Poly strip hair is also suitable for real-time rendering applications such as, e.g., AR/MR/VR and gaming.

Example Direct Generation of Polygon Strip (Poly Strip) Hair for a Virtual Avatar

FIG. 12 schematically illustrates an example of the generation of polygon strip (poly strip) hair for an avatar 1000. In this example, the animator uses a 3D animation application (e.g., Maya) to generate poly strip hair starting from a model of the avatar 1000 without hair. Rather than placing guide curves on the avatar, the animator places polygon hair strips on the avatar, with one end of the poly strip attached to the hair surface (e.g., the scalp of the avatar).

FIG. 12 shows an example of a poly strip 1210 a comprising a plurality of linked polygons (in this case, quadrilaterals). The number of polygons in the poly strip can be any number, e.g., 1, 2, 3, 6, 12 or more. The width of the poly strip is typically less than the length of the poly strip so that the poly strip can be textured (described below) to look like a tuft or clump of hair.

The animator can use the 3D animation application to position the poly strips 1210 on the hair surface of the avatar 1000. An initial stage of the process is shown in FIG. 12 where a number of poly strips 1210 have been placed on the left side of the avatar's scalp (this region will form one of the hair region parts of the avatar's hair). Poly strip characteristics can be adjusted by the animator so that the poly strips are positioned and shaped to represent the desired hair groom. For example, poly strip length, width, rotation, twist, taper, curvature, etc. can be adjusted. The number of polygons used in the poly strip, either along a longitudinal axis of the strip or perpendicular to the strip can be adjusted. The animator places the polygon strips 1210 on the avatar, one by one, until the result appears acceptable (to the animator's subjective design aesthetic).

Hair textures can be applied to each of the poly strips to give each poly strip the appearance of hair. The hair textures can represent different types of hair design (see, e.g., the hair texture examples in FIG. 16A). FIG. 12 shows a UV layout 1220 of several different hair textures 1225 that can be applied to the poly strips. The arrowed lines from the layout 1220 to the poly strips 1210 indicate which hair texture is applied to which poly strip. Similar to the laying out of the poly strips, the animator will assign the hair textures 1225 one by one to the poly strips 1210 until the result appears acceptable (to the animator's subjective design aesthetic). If the animator adjusts the poly strip geometry, then the animator typically has to go back and adjust the texture (and vice versa), leading to a time-intensive, iterative process for directly creating a poly strip hair groom for the avatar.

Overview of Generation of Poly Strip Hair from Strand-Based Hair for a Virtual Avatar

Although generating poly strip hair directly can be performed (without starting from strand-based hair), it is a time-consuming, iterative process that typically requires substantial animator input.

Accordingly, embodiments of the systems and methods described herein can generate poly strip hair for an avatar using as an input previously-generated strand-based hair for the avatar (see, e.g., the example of strand-based hair 1170 in FIG. 11E). Some such embodiments can (A) populate the poly strips on the hair regions of the avatar based on the high-fidelity strand-based hair data, optionally factoring in hairline or parting for better coverage of poly strips on the avatar. After the poly strips have been populated, the systems and methods can (B) perform final texture synthesis and automatic UV layout, which can significantly reduce the number of iterations needed to obtain the final poly strip hair groom.

-   -   (A) Generation of Poly Strips

As described above, high-fidelity strand-based hair can include, for example, over 100,000 strands, just like real human hair. The systems and methods described herein can use a subset of these strands as a base form, e.g., a uniform sampling of 0.1% of 100,000 strands provides 100 strands, which can form a reasonable base. Some of the sampled strands may be the guide curves that were originally modeled by the animator to provide instances for the strand-based hair. The systems and methods automatically generate a poly strip along each sampled strand. The poly strips can be further editable, permitting adjustment of any of the poly strip characteristics (e.g., number of polygons, orientation, twist, flatness, clumpiness, etc.) if desired.

In some cases, uniform distribution of the strands may be suitable for some (e.g., the inner regions of the hair) but may not provide sufficient coverage for other regions (e.g., the outer regions (e.g., near parts or hairlines)). In such cases, the systems and methods can add more strands along or near to regions with insufficient coverage (e.g., the hairline or parts). For example, the strand-based hair data can include a hair mask for the avatar's scalp geometry to define where hair strands are populated (see, e.g., the hair mask 1120 described with reference to FIG. 1C). The systems and methods can perform contour detection on the hair mask to determine a hairline curve and can perform extra sampling of the strands near the hairline curve to populate additional strands or poly strips near the hairline. Parting can be performed similarly, e.g., if there is a hair region map to define parting (see, e.g., the hair region 1155 described with reference to FIG. 11D), contour detection can determine a part line (e.g., the part line 1150), and the systems and methods can sample the strand hair data and add more strands or poly strips on each side of the part line.

FIG. 13 shows an example of poly strip hair generation using strand-hair as an input. Avatars 1302 and 1306 show poly strip generation without taking into consideration the avatar's hairline and part. Avatars 1304 and 1308 show poly strip generation that includes consideration of the avatar's hairline and part.

The avatar 1302 shows base sample points 1310 a distributed over the region of the avatar's scalp corresponding to a hair mask. The sample points 1310 a come from a uniform sampling of hair strands in the previously-generated, high-fidelity strand-based hair data for the avatar. The points show positions of the base ends of the sampled hair strands, which are attached to the hair region (e.g., the scalp) to be populated with poly strips. Avatar 1306 shows the poly strips 1210 a that are generated at locations of the sample points 1310 a. It can be seen from the avatar 1306 that there are gaps in the poly strip distribution near the hairline (over the forehead) and along a part that is near the upper left region of the avatar's scalp.

The avatar 1304 shows additional sample points 1310 b populated near the hairline and additional sample points 1310 c populated near the part. The additional sample points 1310 b and 1310 c can be obtained by sampling the strand-based hair data near the hairline and part, which as described above, can be obtained by contour detection from the strand-based hair data. The avatar 1308 shows the poly strips 1210 a (from the uniform sampling shown in the avatar 1306), as well as additional poly strips 1210 b (near the hairline) and additional poly strips 1210 c (near the part) generated based on the additional sample points 1310 b and 1310 c, respectively. It can be seen by comparing the avatar 1308 to the avatar 1306 (without hairline or part processing) that the poly strips 1210 a, 1210 b, 1210 c provide more realistic coverage of the avatar's hair region, including the hairline region and the part region. For example, the hairline and part processing fills in gaps in the avatar's hair that would otherwise occur and would make the avatar look less natural.

(B) Texture Layout

Texture resources are typically limited in real-time applications (e.g., AR/VR/MR or gaming); thus, hair textures and poly strip geometry UVs are typically laid out in a single square UV space—see, the example UV texture space 1220 described with reference to FIG. 12.

In techniques that do not use strand-based hair data as an input, the layout of the poly strips is typically predetermined by the animator, and texture or UVs are adjusted as needed while placing and modeling the poly strips. However, this direct poly strip process requires many adjustments and iterations by the animator. For example, if an animator wants to widen (or narrow) a poly strip, an associated hair texture will be stretched (or shrunk) and may no longer look correct when applied to the poly strip. In such a case, the animator typically adds a new texture, lays out all the textures in a square UV space 1220, which results in adjusting the UVs on all of the poly strips, which is time-consuming and inefficient.

Accordingly, various embodiments of the systems and methods described herein defer the final texture/UV layout to the end of the hair generation process, which effectively decouples texturing from the poly strip modeling process.

For example, a hair texture library tool can be created to populate many generic hair textures (see, e.g., the example described with reference to FIG. 16A). Those textures can be generated with a textural modeling tool, which allows an animator to add variations easily, for example, greater or less curliness, longer or thinner hair, clumpiness or noise, etc. The hair textures can be managed with a common set of hair texture properties (e.g., length, width, curliness, clumpiness, etc.), so that the system can later find the best or closest matching texture for each poly strip (which also may have corresponding properties) based on the properties in common.

These generic hair textures may not have a specific UV layout, for example, all such generic textures can occupy a square UV space so that they can be assigned to any of the poly strips created in the modeling process. The square UV space can range from 0 to 1 in each of the U and V coordinates. An advantage of this process is that an animator can adjust geometry or texture assignment as desired, without also adjusting the UV layout. In some implementations, the poly strips can also be laid out in a square UV space (e.g., from 0 to 1 in each U, V coordinate), which advantageously allows an animator to assign any of the hair textures to any of the poly strips. Additional aspects of the UV mapping of hair textures is described below with reference to FIGS. 16B-16D.

Once poly strip geometry editing (e.g., UV mapping) and texture assignment are finished, the system can consolidate the textures to a single UV space and adjust the poly strip UVs accordingly. For example, the system can scale each texture based on the poly strip size (or other factors), and lay out the scaled textures in a single square UV space using a rectangle packing algorithm. For example, larger poly strips appear larger on the avatar when rendered and thus their scaling factor can be selected to be greater than smaller poly strips (which do not affect rendered appearance as much).

For example, the scaled textures can be consolidated to a single square texture in an improved or optimal way by performing 2D bin packing of the scaled, rectangular (or other shaped) textures. The 2D bin packing algorithm can pack rectangles together into a square container and can be used to lay out and scale the textures into a given resolution efficiently. A variety of bin packing algorithms can be used including, e.g., a first-fit algorithm, a greedy approximation algorithm, a best fit decreasing algorithm, a first fit decreasing algorithm, etc.

In some implementations, the bin packing algorithm can comprise a process that recursively packs an area in UV space with textures of different sizes. In some such implementations, the area in UV space may be too small to pack all of the textures into the target region (e.g., a 0 to 1 UV square). If a texture does not fit into the target region, the process can expand the size of the target region to fit the texture. After all of the textures have been packed into the target region, the process can rescale the expanded size of the target region to a desired size (e.g., from 0 to 1 in each of the U, V coordinates). The bin packing algorithm can be written in a high-level interpreted programming language, such as Python.

After the layout of the textures is performed, the UVs of the poly strips may, in some cases, need to be adjusted accordingly. In some cases, the UVs of the poly strips prior to this texture layout may be square in UV space and can be adjusted for the final texture layout. For example, the system can record all transform operations of the texture packing process and apply the transforms to the poly strips to fit the UVs.

The output texture and the poly strip geometry are then ready to be used in a real-time application. For example, the output texture and the poly strip geometry can be stored in non-transitory storage 260 of the wearable system 200 and applied by the avatar processing and rendering system 690 to render the hair of the avatar. In some implementations, the rendering system 690 can render each texture with extra render passes to improve the appearance of the avatar's hair.

FIG. 14 illustrates an example of the hair texture consolidation process. Panel (a) illustrates textures that will be applied to poly strips of the avatar. In this case, six textures are shown, however this is not a limitation and is for illustrative purposes. Other numbers of textures are usable. These textures can each be stretched or scaled to fit the square layout in UV space (e.g., extend from 0 to 1 in UV space), for ease of application to the poly strips (which also may also be stretched or scaled to fit the square layout in UV space). Panel (b) shows the individual textures from panel (a) after scaling based on, e.g., poly strip sizes (e.g., length, width, area, etc.). Note that the scaling represents the hair textures in a more natural and realistic manner as compared to UV space fitting shown in panel (a). For example, the scalings may restore the original length to width ratio of each hair texture. Other scalings can be used. Panel (c) shows the scaled textures packed into a single square texture (extending from 0 to 1 in both U and V directions). The packing has maintained the relative size (e.g., length to width ratio) of the textures shown in panel (b). Each of the individual, scaled textures can be assigned to one or more poly strips. The square texture region can be accessed by the real-time rendering engine and applied to the poly strips generated for the avatar.

Thus, embodiments of the disclosed techniques that utilize high-fidelity strand-based hair data to initialize the poly strip hair generation process can be executed more rapidly and efficiently, with less human animator input and fewer iterations, to generate avatar hair. Accordingly, using the strand-based hair data as an input and deferring the UV texturing layout process until after the poly strips have been generated provide substantial advantages over direct poly strip hair generation (without pre-generated strand hair data) and permit real-time rendering of avatar hair (which is unfeasible with strand-based hair).

Additional details of various embodiments of poly strip hair generation techniques are described below.

Example Implementations of Poly Strip Hair Generation from Strand-Based Hair for a Virtual Avatar

Example Poly Strip Generation Techniques

The system can access high-fidelity, strand-based hair data that has been previously generated for an avatar. The strand-based hair data can include a hair mask defining a region where hair grows on the avatar (the hair mask is sometimes referred to as a hair map or UV map), guide curves for the strands, and/or the instanced strands. Note that texture is not needed at this stage, because the strand-based hair is a 3D model and the numerous (e.g., 100,000+) strands mimic real hair strands. Textures are discussed below, because they are applied to poly strips.

A distribution of the strands from the strand-based hair data is selected. The distribution can be a uniform distribution across the hair mask or the hair regions. For example, a fraction f of the strands can be selected, where in various embodiments, f can be in a range from 0.05% to 5%, e.g., 0.1%, 0.2%, 0.5%, etc. Examples of the base points of the strands (e.g., an end of the strand or its guide curve attached to the hair surface (e.g., the scalp)) are illustrated in FIG. 13 as sample points 1310 a. FIG. 15A shows another example of an avatar 1000 with the strands 1110 sampled from the strand-based hair data.

Optionally, the strand-based hair data can be analyzed to determine hairline(s), part(s), or hair region(s) (e.g., on either side of a part, where hair may fall differently). For example, contour detection can be performed to determine the hairline or the parts. FIG. 15B shows an example of the hairline 1140 and the part line 1150 determined from the strand-based hair data for the avatar shown in FIG. 15A. In this example, the part line 1150 extends slightly beyond the hairline 1140 on the avatar's forehead.

Additional base points (e.g., strands) can be added near the hairline or the parts. The density of the added base points can be selected to provide a desired distribution near these regions. Rather than adding the base points exactly along the hairline or part (which would look artificial), the system can distribute the added points randomly in the vicinity of the hairline or the part, e.g., within a small distance orthogonal to the hairline or part (e.g., 5 mm, 10 mm, 15 mm, etc.). The system can distribute these added base points with a fall off from the hairline or part, e.g., the probability of an added point decreases in a direction away from the hairline or part. Examples of the additional base points added near the hairline and part line are shown as the sample points 1310 b, 1310 c in FIG. 13.

By sampling the strand-based hair data (with or without processing hairlines or parts), the system arrives at a number N of strands (which may include guide curves) distributed over the hair mask area of the avatar's head. The number N may be in a range from 50 to 200 (e.g., about 100) or in a range from about 50 to 500, or more, in various embodiments (e.g., from 100 to 10,000 with sufficiently powerful processors).

Poly strips can then be generated from the N strands. The system, in effect, “extrudes” a poly strip along each strand. The automated process generates a series of polygons (e.g., quadrilaterals) along the strand so that the poly strip follows the shape of the strand. Each poly strip for a single avatar can be generated in parallel or in series.

FIG. 15C shows an example of a poly strip 1210 that follows the shape of a strand 1110. The center of the poly strip follows the curvature of the strand. In the U-V coordinate system 1511, the width (in the U-direction) of the poly strip is two polygons (e.g., quadrilaterals), with one polygon on either side of the strand. The length (in the V-direction) of the poly strip is 40 polygons, so that there are a total of 80 polygons in this example of a poly strip. In alternate embodiments, the poly strip can be one polygon wide or greater than two polygons wide, or the length of the poly strip can be greater than or lesser than 40 polygons.

The orientation of the poly strip can be determined via the following process described with reference to FIG. 15D. FIG. 15D shows an avatar 1000 with a single hair (for illustration) represented as a strand 1110 starting at the hair surface (the scalp) and extending away from the avatar's head. The strand can be represented as a sequence of points.

The orientation of the poly strip can be defined based on two vectors: a hair vector and a reference vector. The hair vector 1532 ({right arrow over (H)}) can be defined as starting from the hair surface (e.g., the scalp in this example) at the base 1530 of the strand 1110 and extending to the next point 1534 in the list of points defining the strand 1110. The hair vector 1532 therefore approximates and is representative of the initial direction of the strand at the scalp. The reference vector 1536 ({right arrow over (R)}) can be defined along the normal direction at the hair surface (e.g., the scalp) at the base 1530 of the strand 1110. The orientation of the poly strip can be determined to be in the direction of the vector cross product of the hair vector 1532 with the reference vector 1536, e.g., in the direction shown by vector 1538 ({right arrow over (H)}×{right arrow over (R)}). Thus, the orientation is in a direction perpendicular to a plane containing {right arrow over (H)} and {right arrow over (R)}. Alternate methods of determining the orientation of a poly strip can be used.

After extrusion, an animator optionally can use a 3D animation application to further adjust the properties of the poly strip 1210 (for example, by adjusting user-selectable widgets such as sliders, dropdown boxes, etc. in a plug-in to the animation application). For example, the width, length, number of polygons in U or V, taper, twist, rotation, etc. can be adjusted as desired. The curvature or orientation of the strand, and its associated poly strip, can also be adjusted in the 3D animation application with animator input. For example, as shown in FIG. 15C, an x-y-z coordinate system 1510 and control points 1514 (shown as solid dots) can be placed at a desired position along the strand and then used to adjust the orientation or curvature at that position. Such animator input is optional and not required.

FIG. 15E shows an example of the avatar 1000 after the poly strips 1210 have been generated and applied to the hair surface on the avatar's head. As described above, an end of each poly strip 1210 is placed on the hair surface where a base of a sampled strand (with or without additional sampling for hairline or parts) is located. Each of the poly strips is then ready for texturing.

Example Texturing Techniques

A hair texture library tool can be created to populate many generic hair textures. Those textures can be generated with a textural modeling tool, which allows an animator to add variations easily, for example, greater or less curliness, longer or thinner hair, clumpiness or noise, etc. The hair textures can be managed with a common set of hair texture properties (e.g., length, width, clumpiness (primary or secondary (clumps within the primary clumps)), density, curliness, etc.), so that the system can later find the best or closest matching texture for each poly strip based on the common hair texture properties. The texture modeling tool can be configured to work with high-fidelity, strand-based hair. For example, an animator can set parameters for the high-fidelity strand hair (e.g., length, density, width, noise, clumpiness, etc.) and the tool can render that hair (e.g., to create a 2D image of the modeled hair) into a hair texture. The hair texture can be saved in a hair library provided by the texture tool (see, e.g., FIG. 16A). Optionally, some of the textures can be re-rendered within the texture tool to add embellishments to the hair. For example, a hair shader can be used to make the strand-based hair appear more like real hair (e.g., hair at the root may look different than at tip, outside of the hair may appear slightly transparent compared to the inside of the hair, etc.). Although the textures described herein are for hair, this is for illustration and is not a limitation. The textures in the library that are to be applied to poly strips could be any type of texture that the animator wants to create for the avatar, e.g., hair, fur, skin, tentacles, etc.

FIG. 16A is a screenshot of an example hair texture library, which can be provided by the 3D animation application. An animation system (e.g., Maya) can be configured to provide a user interface, which may accept user input to modify strand-based hair properties, hair texture properties, etc., which may be stored in the hair texture library. The hair texture library can include dozens (or more) generic hair textures with different styles and properties. The hair texture library can present thumbnail images of the generic hair textures. In FIG. 16A, 30 hair textures are shown and the screen could be scrolled downward to access additional textures. An animator can select a hair texture to find out its properties. In this example, the hair texture 1225 b, “hair_1”, is selected (indicated by bold highlighting), and its properties are listed in the attributes panel on the right. Individual properties can be selected and edited. For example, “hair_1” has a length of 10.0, a width of 2.0, a density of 100.0, a strand width of 0.02, primary and secondary clump values of 0.50, noise value of 0.50, and a curl value of 0.0.

Each poly strip has properties associated with it (e.g., length, width, clumpiness, noise, curliness, density, etc.). Some poly strip properties (e.g., clumpiness) can be based on an associated property of the pre-generated high fidelity, strand-based hair. For example, the system can query the strand-based hair data to identify a value of the associated property for the poly strip. In an analogous fashion, each hair texture has properties associated with it (e.g., the properties that were specified in the texture tool when the texture was created).

The system can make an initial assignment of hair textures to poly strips by matching their properties. For matching, the system can use a matching criterion to find the closest match, which may or may not be an exact match. The matching criterion may be a match between one or more poly strip properties and one or more hair texture properties. The matching criterion can be based on a match between a ranked set of properties or a weighted set of the properties. For example, the closest match may be based on a set of weights for the various properties. Larger weights can be given to properties that have a larger impact on the rendered appearance of the hair (e.g., properties that may be more visually noticeable to a viewer). As one non-limiting, illustrative example, clumpiness tends to have a relatively large impact and can be given a larger weight, whereas length tends to be less important and may be given a lower weight. This relative low weight for the length parameter is used, because if the texture is too short (or too long) for the poly strip, the rendered hair will simply appear slightly shorter (or longer) than expected, which may not even be visually noticeable. Note that at this point in the hair generation process, only the UV properties of the poly strips on the avatar are needed, a UV map for the avatar's head is not needed.

After this assignment, each poly strip used for the avatar will have a corresponding texture assigned to it. FIG. 16B is a screenshot of the example hair texture library showing the assigned textures for the avatar. In this example, seven textures from the library have been assigned to poly strips for the avatar (this is for illustration, a different number of hair textures could match in other examples). In this example, the hair texture 1225 c, “hair_11”, is selected (indicated by bold highlighting), and its properties are listed in the attributes panel on the right. Individual properties can be selected and edited. For example, “hair_11” has a length of 10.0, a width of 2.0, a density of 100.0, a strand width of 0.02, primary clump value of 0.0 (note the hair is fairly uniform and unclumped), secondary clump values of 0.50, noise value of 0.50, and a curl value of 0.0. Thus, the hair texture 1225 c has a length to width ratio indicating it is relatively long.

As described above, it can be advantageous to pack all of the poly strip textures used for a particular avatar into a single square UV space. Real-time rendering systems can read square images more quickly than non-square images. Further, it is quicker and more efficient for the real-time rendering system to read a single data structure (e.g., representing the square UV space packed with all the poly strip textures used for the avatar) than to read separate data structures for each of the individual textures used for the avatar. Thus, for rendering efficiency, it can be advantageous to provide a single square in the UV space that contains all the hair textures that will be applied to the poly strips for the avatar. Some embodiments of the hair generation system therefore can pack all the avatar's hair textures into a single UV space (preferably square in UV coordinates) using a 2D bin packing process.

The bin packing process can scale the textures based on scaling factor such as, e.g., a relative importance of each texture to the avatar's visual appearance. Larger poly strips (e.g., with larger area) tend to be more important visually, because larger poly strips draw more attention from the viewer (e.g., such poly strips occupy more display space when rendered). Textures corresponding to these larger poly strips preferably should occupy relatively more space in the packed UV square. For example, in some implementations, all the poly strips are initially created with the same default width but can have different lengths. Longer poly strips may be placed at the top of the avatar's head (where hair tends to be longer), while shorter poly strips may tend to be placed at the sides or bottom of the avatar's head. In such implementations, larger poly strip length is a proxy for larger poly strip area, and the textures associated with the longer poly strip lengths can be assigned greater area in the packed UV square. As the packing is performed, the transforms applied to the textures (to pack them into the square) can be applied to the UV values of the corresponding poly strips (or the transforms stored and applied to the poly strips afterwards).

After the bin packing process is performed, all of the textures that have been matched to one or more of the avatar's poly strips will have been packed into a single square UV space (see, e.g., the example square 1220 in FIG. 14). The square can be scaled to unit coordinates (e.g., between 0 and 1) in the UV space. The UV square packed with the textures, and the poly strips, can be communicated to the wearable display system 200 (e.g., via the network 990) and stored by the local processing and data module 260, for use by the avatar processing and rendering system 690 to render the avatar's hair for real-time AR/VR/MR applications.

FIG. 16C shows an example of textured poly strip hair where this bin packing of the hair textures has not yet been performed. In FIG. 16C, the matched hair textures (from FIG. 16B) have been applied to the poly strip arrangement for the avatar 1000 shown in FIG. 15E. The left side of FIG. 16C is a 3D view of the avatar 1000 with textured hair 1604 a and also shows one of the poly strips 1210 d highlighted. The right side of FIG. 16C is a UV view of this texture 1225 c applied to the poly strip 1210 d. Note how the texture fills the entire UV space 1220 a (e.g., fits the entirety of the 0 to 1 UV square), which does not include any other textures. This UV view of the hair texture 1225 c is a normalized view (e.g., stretched to fit the square UV layout 1220 a) so that the hair texture 1225 c can be assigned to any of the poly strips, which also can be represented as square UV spaces.

FIG. 16D shows an example of textured poly strip hair where this bin packing of the hair textures has been performed, and the hair textures have been consolidated to a single square. In FIG. 16D, the matched hair textures (from FIG. 16B) have been applied to the poly strip arrangement for the avatar 1000 shown in FIG. 15E. The left side of FIG. 16D is a 3D view of the avatar 1000 with textured hair 1604 b and also shows one of the poly strips 1210 d highlighted. The right side of FIG. 16D is a UV view of the layout of the packed textures in the UV square 1220 b. Note how the texture 1225 c that has been applied to the poly strip 1210 fills only a portion of the UV space 1220 b (e.g., compare to the square 1220 a shown in FIG. 16C where the texture 1225 c fills the entire UV space). Thus, the ratio of length to width of this texture 1225 c in the UV space 1220 b maintains its original ratio of length to width, as shown in FIG. 16B. Further, since the texture 1225 c corresponds to a relatively large poly strip 1210, the texture 1225 c has been assigned a larger area of the square 1220 b (as compared to the other textures packed into the square).

Although it is computationally efficient and advantageous to pack the hair textures into a square UV space, this is not a requirement, and in other implementations, the hair textures can be packed into a data space with a different UV geometry (e.g., rectangular).

Example Processes for Generating Polygon Strip Hair from Strand-Based Hair

FIG. 17 is a flowchart that illustrates an example process 1700 for generating textured poly strip hair from strand-based hair generated for an avatar. The example process 1700 can be performed by one or more computing systems including the remote processing module 270, the local processing and data module 260, the avatar processing and rendering system 690 of FIG. 6B, the remote computing system 920 of FIG. 9A, other suitable hardware computing device(s), and combinations of these and other computing devices.

At block 1704, the process 1700 accesses strand-based hair data that has been generated for an avatar. The strand-based hair data can include information on the strands (including guide curves and instanced strands) and a hair mask defining a region on the avatar where the hair is to be generated. Optionally, the strand-based hair data can include information on hairlines or hair part(s), hair regions where hair may fall differently than in other regions (e.g., regions on either side of a part), etc. As described below with reference to block 1712, some implementations may generate hairline or hair part data from the strand-based hair data, if that data does not already include the hairline or part line data.

At block 1708, the process 1700 samples the strand-based hair data to identify a subset (less than all of the strands) of strands. The sampling can be a uniform random sampling over the region of the hair mask. As described above, the sampling fraction can be in a range from about 0.01% to about 1% (e.g., 0.1% in some embodiments) to provide a suitable number of strands for poly strip generation.

If, for example, the strand-based hair data does not already include data on hairlines or part lines, at optional block 1712, the process 1700 can identify a hairline or a part line. Some hair grooms will contain multiple hair lines (e.g., a hairline around the forward, sides, and back of the head and another hairline for a central bald spot). Some hair grooms will not contain a part and others may contain multiple parts. The process can use, e.g., contour detection applied to the strand-based hair data to identify hairline(s) or part line(s).

In some cases, the sampling at block 1708 may not provide sufficient numbers of strands or there may be patches without strands near a part line, a hair line, or other region. At optional block 1716, the process 1700 can perform additional sampling of the strand-based hair data to provide additional strands near hairline(s) or part line(s). For example, additional strands within a threshold distance (e.g., 1 mm to 20 mm) from the hairline(s) or part line(s) are identified. After performance of blocks 1704 to 1716, the number N of sampled strands may be in a range from about 50 to about 500, in some embodiments. These strands, e.g., their shape and base position on the hair region (e.g., scalp) of the avatar serve can serve as the basis for generation of the poly strips.

Accordingly, at block 1720, the process 1700 generates a poly strip for each of the sampled strands. A poly strip comprises a plurality of polygonal regions (e.g., quadrilateral), and an example poly strip 1210 is shown for an example strand 1110 in FIG. 15C. One end (called the base) of each poly strip will be positioned on the avatar's hair region where the base of the corresponding strand is located. An initial orientation of a poly strip can be determined using the vector cross product technique described with reference to FIG. 15D or other suitable method. Some implementations of the process 1700 can accept user-input to adjust the positioning, orientation, properties, and so forth of the poly strips.

Each poly strip can be associated with poly strip properties such as, e.g., length, width, clumpiness, curliness, noise, density, etc.).

At block 1724, the process 1700 accesses a hair texture library comprising a plurality of hair textures that may be applied to the poly strips (generated at block 1720). The hair textures are typically represented in a texture space having orthogonal coordinates commonly called U and V. Each hair texture can comprise an image (e.g., a bitmap) showing a particular style for a tuft or clump of hairs (see, e.g., the hair textures shown in FIG. 16A). Each hair texture can be associated with hair texture properties that roughly (or precisely) correlate with some or all of the poly strip properties. The hair texture properties can include e.g., length, width, clumpiness, curliness, noise, density, etc.

At block 1732, the process 1700 matches hair textures from the hair library with the poly strips. The matching process can compare poly strip properties with hair texture properties to match a particular hair texture to one or more poly strips for the avatar. The match can, in some case, be the closest match and not an exact match. Since there are typically multiple properties being matched (e.g., clumpiness, length, curliness, etc.), the process 1700 can access a weight for the various properties so that the properties that have a larger impact on rendered hair appearance have a larger weight than the properties that have a smaller impact on hair appearance (which are given a smaller weight). As described above, clumpiness tends to have a larger impact on appearance than length, so clumpiness may be assigned a larger weight than length in the matching process. The output from block 1732 is a set of hair textures that are matched to one or more of the poly strips. FIG. 16B shows an example of matched hair textures. Any suitable matching method can be used in order to obtain the desired output.

At block 1736, the process 1700 packs the set of matched hair textures into a data structure that represents a region of texture space including the matched hair textures. The data structure provides for efficient and rapid data access by a rendering engine. For example, the region in texture space can be square and have unit lengths in two orthogonal directions (e.g., from 0 to 1 in each U, V coordinate of the texture space). The data structure containing the packed hair textures can comprise a bitmap. Although a square region of texture space is preferred (for computational efficiency), this is not a limitation and other shaped regions can be used (e.g., rectangular). A bin packing algorithm can be used to pack each of the set of matched textures into the texture space region. In some implementations, prior to packing, the process 1700 scales each of the matched textures based on a property associated with the texture. For example, the property used for scaling can be the area of the texture, the length of the texture (which might be a proxy for area if all textures have the same width), etc. FIG. 14, panel (b), shows an example of scaled textures 1225.

In some implementations, the scaled textures can be consolidated to a single square texture in texture space in an improved or optimal way by performing 2D bin packing of the scaled, rectangular textures. The 2D bin packing algorithm can pack rectangles together into a square container in texture space and can lay out the textures into a given resolution efficiently. A variety of bin packing algorithms can be used including, e.g., a first-fit algorithm, a greedy approximation algorithm, a best fit decreasing algorithm, a first fit decreasing algorithm, etc. FIG. 14, panel (c), illustrates an example of a square region 1220 of texture space packed with the set of matching textures, and FIG. 16D shows another example of a packed region 1220 b.

At block 1740, the process 1700 outputs the poly strips and the packed texture space, for example, to non-transitory storage. At optional block 1744, a real-time rendering system (e.g., the wearable system 200) can access the data files output at block 1740 and render the avatar with textured poly strip hair. The real-time rendering system is, in many cases, a different system than the system that performs blocks 1704 to 1740 of the process 1700. For example, with reference to FIG. 9A, poly strip generation and texture packing can be performed by the remote computing system 920 (e.g., in the cloud), and the poly strip and packed texture data (e.g., from block 1740) can be communicated, via the network 990, to individual wearable devices 930 b, 930 c or another display system 930 a for real-time rendering.

Additional Aspects

1. A system comprising: non-transitory computer storage configured to store: strand-based hair data for rendering hair of a virtual avatar, the strand-based hair data comprising strands indicative of shapes of strands of hair and a hair mask indicative of a region on the head of the avatar where hair is to be generated; and a library of hair textures, each of the hair textures comprising an image of a hair style, each of the hair textures associated with hair texture properties; and a hardware processor in communication with the non-transitory computer storage, the hardware process programmed to: sample the strand-based hair data to obtain a first set of strands in a region of the hair mask; determine, from the strand-based hair data, a hairline or a part line; sample the strand-based hair data to obtain a second set of strands in a region near the hairline or in a region near the part line; generate a set of polygon strips (poly strips), wherein each poly strip is based on a strand in the first set or the second set of strands, each poly strip comprising at least one polygon, and each poly strip associated with poly strip properties; match hair textures from the library of hair textures with the set of poly strips to obtain a set of matched hair textures, wherein a matched hair texture is usable to provide texture to at least one poly strip from the set of poly strips, and wherein the matching is based on a matching criterion between a poly strip property and a hair texture property; pack the set of matched hair textures into a texture space data structure representing a region of texture space; and output the texture space data structure and the set of poly strips.

2. The system of aspect 1, wherein the strand-based hair data comprises at least 100,000 strands.

3. The system of aspect 1 or aspect 2, wherein the hair texture properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

4. The system of any one of aspects 1 to 3, wherein the poly strip properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

5. The system of any one of aspects 1 to 4, wherein to sample the strand-based hair data, the hardware processor is programmed to perform uniform sampling at a sampling fraction of a total number of strands in the strand-based hair data.

6. The system of aspect 5, wherein the sampling fraction is in a range of 0.01% to 1%.

7. The system of any one of aspects 1 to 6, wherein to determine a hairline or a part line, the hardware processor is programmed to perform contour detection.

8. The system of any one of aspects 1 to 7, wherein a total number of strands in the first set and the second set is in a range from about 50 to 500.

9. The system of any one of aspects 1 to 8, wherein a central region of each poly strip follows a curvature of the strand on which the poly strip is based.

10. The system of any one of aspects 1 to 9, wherein a base point of each poly strip is located at a base point of the strand on which the poly strip is based.

11. The system of any one of aspects 1 to 10, where the hardware processor is further programmed to determine an orientation of each poly strip in the set of poly strips.

12. The system of aspect 11, wherein to determine the orientation of the poly strip, the hardware processor is programmed to: calculate a hair vector representative of an initial direction of the strand on which the poly strip is based; calculate a reference vector representative of a normal direction to the hair region at the base of the strand; and determine the orientation based on a vector cross product of the hair vector with the reference vector.

13. The system of any one of aspects 1 to 12, wherein the hardware processor is further programmed to accept user input to modify an orientation, attribute or property associates with a poly strip.

14. The system of any one of aspects 1 to 13, wherein the matching criterion is the closest match between a hair texture from the library of hair textures and a poly strip from the set of poly strips.

15. The system of any one of aspects 1 to 14, wherein to match hair textures, the hardware processor is programmed to apply weightings to hair texture properties and poly strip properties.

16. The system of aspect 15, wherein a weight for clumpiness is larger than a weight for length.

17. The system of any one of aspects 1 to 16, where to pack the set of matched hair textures, the hardware processor is programmed to scale each of the matched hair textures based on a scaling factor.

18. The system of aspect 17, wherein the scaling factor comprises an area of the hair texture or a length of the hair texture.

19. The system of any one of aspects 1 to 18, wherein to pack the set of matched hair textures, the hardware processor is programmed to apply a two-dimensional (2D) bin packing algorithm.

20. The system of aspect 19, wherein the hardware processor is programmed to: determine that a matched hair texture is too large to fit into the region of texture space; expand the region of texture space from an initial size to an expanded size sufficient to fit the matched hair texture; pack the matched hair texture into the region of texture space having the expanded size; and rescale the region of texture space from the expanded size to the initial size.

21. The system of any one of aspects 1 to 20, wherein the region of texture space comprises a square region.

22. The system of any one of aspects 1 to 21, wherein to pack the set of matched hair textures, the hardware processor is programmed to determine a transform for at least one hair textures in the set of matched hair textures.

23. The system of aspect 22, wherein the hardware processor is further programmed to apply the transform to the poly strips that match the at least one hair texture.

24. The system of any one of aspects 1 to 23, wherein the virtual avatar is a virtual representation of a human, an animal, a creature, or an object.

25. The system of any one of aspects 1 to 24, wherein to output the texture space data structure and the set of poly strips, the hardware processor is programmed to communicate the texture space data structure and the set of poly strips to a wearable display device.

26. The system of aspect 25, further comprising the wearable display device, and wherein the wearable display device is programmed to render the hair of the virtual avatar based at least partly on the texture space data structure and the set of poly strips.

27. A system comprising: non-transitory computer storage configured to store: strand-based hair data for rendering virtual hair for a virtual avatar, the strand-based hair data comprising information associated with virtual strands of the virtual hair; and a library of hair textures, each of the hair textures associated with hair texture properties; and a hardware processor in communication with the non-transitory computer storage, the hardware process programmed to: sample the strand-based hair data to obtain a set of virtual strands; generate a set of polygon strips (poly strips), wherein each poly strip is based on a virtual strand in the set of virtual strands, and each poly strip associated with poly strip properties; match hair textures from the library of hair textures with the set of poly strips to obtain a set of matched hair textures, wherein a matched hair texture is usable to provide texture to at least one poly strip from the set of poly strips; pack the set of matched hair textures into a texture space data structure representing a region of texture space; and output the texture space data structure and the set of poly strips.

28. The system of aspect 27, wherein each virtual strand is associated with a strand.

29. The system of aspect 27 or aspect 28, wherein the strand-based hair data comprises information on a hairline or a part line.

30. The system of aspect 27 or aspect 28, wherein the hardware processor is further programmed to determine, from the strand-based hair data, a hairline or a part line

31. The system of aspect 29 or aspect 30, wherein the hardware processor is further programmed to sample the strand-based hair data to obtain additional virtual strands in a region near the hairline or in a region near the part line, the additional virtual strands included in the set of virtual strands.

32. The system of any one of aspects 27 to 31, wherein the set of virtual strands comprises a fraction of the virtual strands in the strand-based hair data, the fraction less than 1%.

33. The system of any one of aspects 27 to 32, wherein the hair texture properties or the poly strip properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

34. The system of any one of aspects 27 to 33, wherein the hardware processor is programmed to determine an orientation of each poly strip in the set of poly strips.

35. The system of aspect 34, wherein to determine the orientation of the poly strip, the processor is programmed to determine a perpendicular direction to a plane containing a first vector representing a direction of the virtual strand on which the poly strip is based and a second vector representing a normal direction to a surface at a base of the virtual strand.

36. The system of any one of aspects 27 to 35, wherein to match hair textures from the library of hair textures with the set of poly strips to obtain a set of matched hair textures, the hardware processor is programmed to identify at least one match between a hair texture property for a virtual strand in the set of virtual strands and a poly strip property for a poly strip in the set of poly strips.

37. The system of any one of aspects 27 to 36, wherein to pack the set of matched hair textures, the hardware processor is programmed to execute a two-dimensional (2D) bin packing algorithm.

38. The system of aspect 37, wherein the hardware processor is programmed to: determine that a matched hair texture is too large to fit into the region of texture space; expand the region of texture space from an initial size to an expanded size sufficient to fit the matched hair texture; pack the matched hair texture into the region of texture space having the expanded size; and rescale the region of texture space from the expanded size to the initial size.

39. The system of any one of aspects 27 to 38, wherein the texture space data structure represents a square region in the texture space.

40. A method comprising: under control of a three-dimensional (3D) animation engine comprising computing hardware: obtaining information on a plurality of virtual strands from strand-based data for a virtual character, generating a plurality of polygon strips (poly strips), wherein each poly strip of the plurality of poly strips is associated with a virtual strand from the plurality of virtual strands; matching a texture with at least one of the poly strips from the plurality of poly strips to obtain a plurality of matched textures; packing the plurality of matched textures into a texture space data structure representing a region of texture space; and outputting the texture space data structure.

41. The method of aspect 40, wherein the strand-based data is for hair of the virtual character.

42. The method of aspect 40 or aspect 41, wherein a poly strip from the plurality of poly strips that is associated with a virtual strand from the plurality of virtual strands has a curvature that follows a curvature of the virtual strand.

43. The method of any one of aspects 40 to 42, further comprising scaling the plurality of matched textures.

44. The method of any one of aspects 40 to 43, wherein obtaining information on a plurality of virtual strands from strand-based data for a virtual character comprises sampling the strand-based data to identify the plurality of virtual strands.

45. The method of any one of aspects 40 to 44, further comprising transforming the plurality of poly strips based at least partly on results from packing the plurality of matched textures into a texture space data structure.

46. The method of any one of aspects 40 to 45, wherein packing the plurality of matched textures into a texture space data structure representing a region of texture space comprises: determining that a matched texture is too large to fit into the region of texture space; expanding the region of texture space from an initial size to an expanded size sufficient to fit the matched texture; packing the matched hair texture into the region of texture space having the expanded size; and rescaling the region of texture space from the expanded size to the initial size.

47. A system comprising: non-transitory computer storage configured to store strand-based hair data for rendering hair of a virtual avatar, the strand-based hair data comprising strands indicative of properties of strands of hair for the virtual avatar; and a library of hair textures, each of the hair textures comprising an image of a hair style, each of the hair textures associated with hair texture properties; and a hardware processor in communication with the non-transitory computer storage, the hardware processor programmed to: access the strand-based hair data; generate a hair texture based at least partly on the strand-based hair data; and store the generated hair texture in the library of hair textures.

48. The system of aspect 47, wherein the hair texture properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

49. The system of claim 47 or aspect 48, wherein the hardware processor is programmed to add at least one embellishment to a strand of the strand-based hair data.

50. The system of aspect 49, wherein the embellishment comprises a variation between a root of a strand and a tip of the strand, or a variation between an outer region of a strand and an inner region of the strand.

51. The system of any one of aspects 47 to 50, wherein the hardware processor is programmed to include a hair shader tool.

52. The system of any one of aspects 47 to 51, wherein the hardware processor is programmed to provide a user interface configured to accept user input.

53. A method comprising: under control of a hardware processor in communication with non-transitory memory: sampling strand-based hair data for rendering hair of a virtual avatar to obtain a first set of strands in a region of the hair mask, the strand-based hair data comprising strands indicative of shapes of strands of hair and a hair mask indicative of a region on the head of the avatar where hair is to be generated; determining, from the strand-based hair data, a hairline or a part line; sampling the strand-based hair data to obtain a second set of strands in a region near the hairline or in a region near the part line; generating a set of polygon strips (poly strips), wherein each poly strip is based on a strand in the first set or the second set of strands, each poly strip comprising at least one polygon, and each poly strip associated with poly strip properties; accessing a library of hair textures, each of the hair textures comprising an image of a hair style, each of the hair textures associated with hair texture properties; matching hair textures from the library of hair textures with the set of poly strips to obtain a set of matched hair textures, wherein a matched hair texture is usable to provide texture to at least one poly strip from the set of poly strips, and wherein the matching is based on a matching criterion between a poly strip property and a hair texture property; packing the set of matched hair textures into a texture space data structure representing a region of texture space; and outputting the texture space data structure and the set of poly strips.

54. The method of aspect 53, wherein the strand-based hair data comprises at least 100,000 strands.

55. The method of aspect 53 or aspect 54, wherein the hair texture properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

56. The method of any one of aspects 53 to 55, wherein the poly strip properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

57. The method of any one of aspects 53 to 56, wherein sampling the strand-based hair data comprises performing uniform sampling at a sampling fraction of a total number of strands in the strand-based hair data.

58. The method of aspect 57, wherein the sampling fraction is in a range of 0.01% to 1%.

59. The method of any one of aspects 53 to 58, wherein determining a hairline or a part line comprises performing contour detection.

60. The method of any one of aspects 53 to 59, wherein a total number of strands in the first set and the second set is in a range from about 50 to 500.

61. The method of any one of aspects 53 to 60, wherein a central region of each poly strip follows a curvature of the strand on which the poly strip is based.

62. The method of any one of aspects 53 to 61, wherein a base point of each poly strip is located at a base point of the strand on which the poly strip is based.

63. The method of any one of aspects 53 to 62, further comprising determining an orientation of each poly strip in the set of poly strips.

64. The method of aspect 63, wherein determining the orientation of the poly strip comprises calculating a hair vector representative of an initial direction of the strand on which the poly strip is based; calculating a reference vector representative of a normal direction to the hair region at the base of the strand; and determining the orientation based on a vector cross product of the hair vector with the reference vector.

65. The method of any one of aspects 53 to 64, further comprising accepting user input to modify an orientation, attribute or property associated with a poly strip.

66. The method of any one of aspects 53 to 65, wherein the matching criterion is the closest match between a hair texture from the library of hair textures and a poly strip from the set of poly strips.

67. The method of any one of aspects 53 to 66, wherein matching hair textures comprises applying weightings to hair texture properties and poly strip properties.

68. The method of aspect 67, wherein a weighting for clumpiness is larger than a weighting for length.

69. The method of any one of aspects 53 to 68, where packing the set of matched hair textures comprises scaling each of the matched hair textures based on a scaling factor.

70. The method of aspect 69, wherein the scaling factor comprises an area of the hair texture or a length of the hair texture.

71. The method of any one of aspects 53 to 70, wherein packing the set of matched hair textures comprises applying a two-dimensional (2D) bin packing algorithm.

72. The method of aspect 71, further comprising: determining that a matched hair texture is too large to fit into the region of texture space; expanding the region of texture space from an initial size to an expanded size sufficient to fit the matched hair texture; packing the matched hair texture into the region of texture space having the expanded size; and rescaling the region of texture space from the expanded size to the initial size.

73. The method of any one of aspects 53 to 72, wherein the region of texture space comprises a square region.

74. The method of any one of aspects 53 to 73, wherein packing the set of matched hair textures comprises determining a transform for at least one hair textures in the set of matched hair textures.

75. The method of aspect 74, further comprising applying the transform to the poly strips that match the at least one hair texture.

76. The method of any one of aspects 53 to 75, wherein the virtual avatar is a virtual representation of a human, an animal, a creature, or an object.

77. The method of any one of aspects 53 to 76, wherein outputting the texture space data structure and the set of poly strips comprises communicating the texture space data structure and the set of poly strips to a wearable display device.

78. The method of aspect 77, further comprising rendering the hair of the virtual avatar based at least partly on the texture space data structure and the set of poly strips.

79. A method comprising: under control of a hardware processor in communication with non-transitory memory: accessing strand-based hair data for rendering virtual hair for a virtual avatar, the strand-based hair data comprising information associated with virtual strands of the virtual hair; and accessing a library of hair textures, each of the hair textures associated with hair texture properties; sampling the strand-based hair data to obtain a set of virtual strands; generating a set of polygon strips (poly strips), wherein each poly strip is based on a virtual strand in the set of virtual strands, and each poly strip associated with poly strip properties; matching hair textures from the library of hair textures with the set of poly strips to obtain a set of matched hair textures, wherein a matched hair texture is usable to provide texture to at least one poly strip from the set of poly strips; packing the set of matched hair textures into a texture space data structure representing a region of texture space; and outputting the texture space data structure and the set of poly strips.

80. The method of aspect 79, wherein each virtual strand is associated with a strand.

81. The method of aspect 79 or aspect 80, wherein the strand-based hair data comprises information on a hairline or a part line.

82. The method of aspect 79 or aspect 80, further comprising determining, from the strand-based hair data, a hairline or a part line

83. The method of aspect 81 or aspect 82, further comprising sampling the strand-based hair data to obtain additional virtual strands in a region near the hairline or in a region near the part line, the additional virtual strands included in the set of virtual strands.

84. The method of any one of aspects 79 to 83, wherein the set of virtual strands comprises a fraction of the virtual strands in the strand-based hair data, the fraction less than 1%.

85. The method of any one of aspects 79 to 84, wherein the hair texture properties or the poly strip properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

86. The method of any one of aspects 79 to 85, further comprising determining an orientation of each poly strip in the set of poly strips.

87. The method of aspect 86, wherein determining the orientation of the poly strip comprises determine a perpendicular direction to a plane containing a first vector representing a direction of the virtual strand on which the poly strip is based and a second vector representing a normal direction to a surface at a base of the virtual strand.

88. The method of any one of aspects 79 to 87, wherein matching hair textures from the library of hair textures with the set of poly strips to obtain a set of matched hair textures comprises identifying at least one match between a hair texture property for a virtual strand in the set of virtual strands and a poly strip property for a poly strip in the set of poly strips.

89. The method of any one of aspects 79 to 88, wherein packing the set of matched hair textures comprises executing a two-dimensional (2D) bin packing algorithm.

90. The method of aspect 89, further comprising: determining that a matched hair texture is too large to fit into the region of texture space; expanding the region of texture space from an initial size to an expanded size sufficient to fit the matched hair texture; packing the matched hair texture into the region of texture space having the expanded size; and rescaling the region of texture space from the expanded size to the initial size.

91. The method of any one of aspects 79 to 90, wherein the texture space data structure represents a square region in the texture space.

92. A method comprising: under control of a hardware processor in communication with non-transitory memory: accessing strand-based hair data for rendering hair of a virtual avatar, the strand-based hair data comprising strands indicative of properties of strands of hair for the virtual avatar; accessing a library of hair textures, each of the hair textures comprising an image of a hair style, each of the hair textures associated with hair texture properties; generating a hair texture based at least partly on the strand-based hair data; and storing the generated hair texture in the library of hair textures.

93. The method of aspect 92, wherein the hair texture properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.

94. The method of claim 92 or aspect 93, wherein further comprising adding at least one embellishment to a strand of the strand-based hair data.

95. The method of aspect 94, wherein the embellishment comprises a variation between a root of a strand and a tip of the strand, or a variation between an outer region of a strand and an inner region of the strand.

96. The method of any one of aspects 92 to 95, further comprising providing a hair shader tool.

97. The method of any one of aspects 92 to 96, further comprising providing a user interface configured to accept user input.

Other Considerations

Each of the processes, methods, and algorithms described herein and/or depicted in the attached figures may be embodied in, and fully or partially automated by, code modules executed by one or more physical computing systems, hardware computer processors, application-specific circuitry, and/or electronic hardware configured to execute specific and particular computer instructions. For example, computing systems can include general purpose computers (e.g., servers) programmed with specific computer instructions or special purpose computers, special purpose circuitry, and so forth. A code module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language. In some implementations, particular operations and methods may be performed by circuitry that is specific to a given function.

Further, certain implementations of the functionality of the present disclosure are sufficiently mathematically, computationally, or technically complex that application-specific hardware or one or more physical computing devices (utilizing appropriate specialized executable instructions) may be necessary to perform the functionality, for example, due to the volume or complexity of the calculations involved or to provide results substantially in real-time. For example, animations or video may include many frames, with each frame having millions of pixels, and specifically programmed computer hardware is necessary to process the video data to provide a desired image processing task or application in a commercially reasonable amount of time. As another example, generating textured poly strip hair as described with reference to FIGS. 13-16D or the process 1700 described with reference to FIG. 17 can be computationally intensive.

Code modules or any type of data may be stored on any type of non-transitory computer-readable medium, such as physical computer storage including hard drives, solid state memory, random access memory (RAM), read only memory (ROM), optical disc, volatile or non-volatile storage, combinations of the same and/or the like. The methods and modules (or data) may also be transmitted as generated data signals (e.g., as part of a carrier wave or other analog or digital propagated signal) on a variety of computer-readable transmission mediums, including wireless-based and wired/cable-based mediums, and may take a variety of forms (e.g., as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames). The results of the disclosed processes or process steps may be stored, persistently or otherwise, in any type of non-transitory, tangible computer storage or may be communicated via a computer-readable transmission medium.

Any processes, blocks, states, steps, or functionalities in flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing code modules, segments, or portions of code which include one or more executable instructions for implementing specific functions (e.g., logical or arithmetical) or steps in the process. The various processes, blocks, states, steps, or functionalities can be combined, rearranged, added to, deleted from, modified, or otherwise changed from the illustrative examples provided herein. In some embodiments, additional or different computing systems or code modules may perform some or all of the functionalities described herein. The methods and processes described herein are also not limited to any particular sequence, and the blocks, steps, or states relating thereto can be performed in other sequences that are appropriate, for example, in serial, in parallel, or in some other manner. Tasks or events may be added to or removed from the disclosed example embodiments. Moreover, the separation of various system components in the implementations described herein is for illustrative purposes and should not be understood as requiring such separation in all implementations. It should be understood that the described program components, methods, and systems can generally be integrated together in a single computer product or packaged into multiple computer products. Many implementation variations are possible.

The processes, methods, and systems may be implemented in a network (or distributed) computing environment. Network environments include enterprise-wide computer networks, intranets, local area networks (LAN), wide area networks (WAN), personal area networks (PAN), cloud computing networks, crowd-sourced computing networks, the Internet, and the World Wide Web. The network may be a wired or a wireless network or any other type of communication network.

The systems and methods of the disclosure each have several innovative aspects, no single one of which is solely responsible or required for the desirable attributes disclosed herein. The various features and processes described above may be used independently of one another, or may be combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. Various modifications to the implementations described in this disclosure may be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the implementations shown herein, but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.

Certain features that are described in this specification in the context of separate implementations also can be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation also can be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. No single feature or group of features is necessary or indispensable to each and every embodiment.

Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. In addition, the articles “a,” “an,” and “the” as used in this application and the appended claims are to be construed to mean “one or more” or “at least one” unless specified otherwise.

As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: A, B, or C” is intended to cover: A, B, C, A and B, A and C, B and C, and A, B, and C. Conjunctive language such as the phrase “at least one of X, Y and Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to convey that an item, term, etc. may be at least one of X, Y or Z. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y and at least one of Z to each be present.

Similarly, while operations may be depicted in the drawings in a particular order, it is to be recognized that such operations need not be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Further, the drawings may schematically depict one more example processes in the form of a flowchart. However, other operations that are not depicted can be incorporated in the example methods and processes that are schematically illustrated. For example, one or more additional operations can be performed before, after, simultaneously, or between any of the illustrated operations. Additionally, the operations may be rearranged or reordered in other implementations. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. Additionally, other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. 

1. A system comprising: non-transitory computer storage configured to store: strand-based hair data for rendering hair of a virtual avatar, the strand-based hair data comprising strands indicative of shapes of strands of hair and a hair mask region indicative of a region on a head of an avatar where hair is to be generated; and a library of hair textures, each of the hair textures comprising an image of a hair style, each of the hair textures associated with hair texture properties; and a hardware processor in communication with the non-transitory computer storage, the hardware process programmed to: sample the strand-based hair data to obtain a first set of strands in the hair mask region; determine, from the strand-based hair data, a hairline or a part line; sample the strand-based hair data to obtain a second set of strands in a region near the hairline or in a region near the part line; generate a set of polygon strips (poly strips), wherein each poly strip is based on a strand in the first set of strands or the second set of strands, each poly strip comprising at least one polygon, and each poly strip associated with poly strip properties; match hair textures from the library of hair textures with the set of poly strips to obtain a set of matched hair textures, wherein a matched hair texture is usable to provide texture to at least one poly strip from the set of poly strips, and wherein the matching is based on a matching criterion between a poly strip property and a hair texture property; pack the set of matched hair textures into a texture space data structure representing a region of texture space; and output the texture space data structure and the set of poly strips.
 2. (canceled)
 3. The system of claim 1, wherein the hair texture properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.
 4. The system of claim 1, wherein the poly strip properties comprise one or more of: a length, a width, a clumpiness, a curliness, a density, or a noise.
 5. The system of claim 1, wherein to sample the strand-based hair data, the hardware processor is programmed to perform uniform sampling at a sampling fraction of a total number of strands in the strand-based hair data.
 6. The system of claim 5, wherein the sampling fraction is in a range of 0.01% to 1%.
 7. The system of claim 1, wherein to determine a hairline or a part line, the hardware processor is programmed to perform contour detection.
 8. (canceled)
 9. The system of claim 1, wherein a central region of each poly strip follows a curvature of the strand on which the poly strip is based.
 10. The system of claim 1, wherein a base point of each poly strip is located at a base point of the strand on which the poly strip is based.
 11. The system of claim 1, where the hardware processor is further programmed to determine an orientation of each poly strip in the set of poly strips.
 12. The system of claim 11, wherein to determine the orientation of the poly strip, the hardware processor is programmed to: calculate a hair vector representative of an initial direction of the strand on which the poly strip is based; calculate a reference vector representative of a normal direction to the hair mask region at the base of the strand; and determine the orientation based on a vector cross product of the hair vector with the reference vector.
 13. The system of claim 1, wherein the hardware processor is further programmed to accept user input to modify an orientation, attribute, or property associated with a poly strip.
 14. (canceled)
 15. The system of claim 1, wherein to match hair textures, the hardware processor is programmed to apply weightings to hair texture properties and poly strip properties.
 16. (canceled)
 17. The system of claim 1, where to pack the set of matched hair textures, the hardware processor is programmed to scale each of the matched hair textures based on a scaling factor.
 18. The system of claim 17, wherein the scaling factor comprises an area of the hair texture or a length of the hair texture.
 19. The system of claim 1, wherein to pack the set of matched hair textures, the hardware processor is programmed to apply a two-dimensional (2D) bin packing algorithm.
 20. The system of claim 19, wherein the hardware processor is programmed to: determine that a matched hair texture is too large to fit into the region of texture space; expand the region of texture space from an initial size to an expanded size sufficient to fit the matched hair texture; pack the matched hair texture into the region of texture space having the expanded size; and rescale the region of texture space from the expanded size to the initial size.
 21. (canceled)
 22. The system of claim 1, wherein to pack the set of matched hair textures, the hardware processor is programmed to determine a transform for at least one hair textures in the set of matched hair textures.
 23. The system of claim 22, wherein the hardware processor is further programmed to apply the transform to the poly strips that match the at least one hair texture.
 24. (canceled)
 25. The system of claim 1, wherein to output the texture space data structure and the set of poly strips, the hardware processor is programmed to communicate the texture space data structure and the set of poly strips to a wearable display device.
 26. The system of claim 25, further comprising the wearable display device, and wherein the wearable display device is programmed to render the hair of the virtual avatar based at least partly on the texture space data structure and the set of poly strips. 27-32. (canceled) 